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Learning Representations for Counterfactual Inference | stat.ML cs.AI cs.LG | Observational studies are rising in importance due to the widespread
accumulation of data in fields such as healthcare, education, employment and
ecology. We consider the task of answering counterfactual questions such as,
"Would this patient have lower blood sugar had she received a different
medication?". We propose a new algorithmic framework for counterfactual
inference which brings together ideas from domain adaptation and representation
learning. In addition to a theoretical justification, we perform an empirical
comparison with previous approaches to causal inference from observational
data. Our deep learning algorithm significantly outperforms the previous
state-of-the-art.
| Fredrik D. Johansson, Uri Shalit and David Sontag | null | 1605.03661 | null | null |
Exponential Machines | stat.ML cs.LG | Modeling interactions between features improves the performance of machine
learning solutions in many domains (e.g. recommender systems or sentiment
analysis). In this paper, we introduce Exponential Machines (ExM), a predictor
that models all interactions of every order. The key idea is to represent an
exponentially large tensor of parameters in a factorized format called Tensor
Train (TT). The Tensor Train format regularizes the model and lets you control
the number of underlying parameters. To train the model, we develop a
stochastic Riemannian optimization procedure, which allows us to fit tensors
with 2^160 entries. We show that the model achieves state-of-the-art
performance on synthetic data with high-order interactions and that it works on
par with high-order factorization machines on a recommender system dataset
MovieLens 100K.
| Alexander Novikov, Mikhail Trofimov, Ivan Oseledets | null | 1605.03795 | null | null |
Detecting Relative Anomaly | stat.ML cs.LG | System states that are anomalous from the perspective of a domain expert
occur frequently in some anomaly detection problems. The performance of
commonly used unsupervised anomaly detection methods may suffer in that
setting, because they use frequency as a proxy for anomaly. We propose a novel
concept for anomaly detection, called relative anomaly detection. It is
tailored to be robust towards anomalies that occur frequently, by taking into
account their location relative to the most typical observations. The
approaches we develop are computationally feasible even for large data sets,
and they allow real-time detection. We illustrate using data sets of potential
scraping attempts and Wi-Fi channel utilization, both from Google, Inc.
| Richard Neuberg and Yixin Shi | null | 1605.03805 | null | null |
Noisy Parallel Approximate Decoding for Conditional Recurrent Language
Model | cs.CL cs.LG stat.ML | Recent advances in conditional recurrent language modelling have mainly
focused on network architectures (e.g., attention mechanism), learning
algorithms (e.g., scheduled sampling and sequence-level training) and novel
applications (e.g., image/video description generation, speech recognition,
etc.) On the other hand, we notice that decoding algorithms/strategies have not
been investigated as much, and it has become standard to use greedy or beam
search. In this paper, we propose a novel decoding strategy motivated by an
earlier observation that nonlinear hidden layers of a deep neural network
stretch the data manifold. The proposed strategy is embarrassingly
parallelizable without any communication overhead, while improving an existing
decoding algorithm. We extensively evaluate it with attention-based neural
machine translation on the task of En->Cz translation.
| Kyunghyun Cho | null | 1605.03835 | null | null |
Asymptotic sequential Rademacher complexity of a finite function class | cs.LG stat.ML | For a finite function class we describe the large sample limit of the
sequential Rademacher complexity in terms of the viscosity solution of a
$G$-heat equation. In the language of Peng's sublinear expectation theory, the
same quantity equals to the expected value of the largest order statistics of a
multidimensional $G$-normal random variable. We illustrate this result by
deriving upper and lower bounds for the asymptotic sequential Rademacher
complexity.
| Dmitry B. Rokhlin | null | 1605.03843 | null | null |
Context-dependent feature analysis with random forests | stat.ML cs.LG | In many cases, feature selection is often more complicated than identifying a
single subset of input variables that would together explain the output. There
may be interactions that depend on contextual information, i.e., variables that
reveal to be relevant only in some specific circumstances. In this setting, the
contribution of this paper is to extend the random forest variable importances
framework in order (i) to identify variables whose relevance is
context-dependent and (ii) to characterize as precisely as possible the effect
of contextual information on these variables. The usage and the relevance of
our framework for highlighting context-dependent variables is illustrated on
both artificial and real datasets.
| Antonio Sutera, Gilles Louppe, V\^an Anh Huynh-Thu, Louis Wehenkel,
Pierre Geurts | null | 1605.03848 | null | null |
Competitive analysis of the top-K ranking problem | cs.DS cs.IT cs.LG math.IT stat.ML | Motivated by applications in recommender systems, web search, social choice
and crowdsourcing, we consider the problem of identifying the set of top $K$
items from noisy pairwise comparisons. In our setting, we are non-actively
given $r$ pairwise comparisons between each pair of $n$ items, where each
comparison has noise constrained by a very general noise model called the
strong stochastic transitivity (SST) model. We analyze the competitive ratio of
algorithms for the top-$K$ problem. In particular, we present a linear time
algorithm for the top-$K$ problem which has a competitive ratio of
$\tilde{O}(\sqrt{n})$; i.e. to solve any instance of top-$K$, our algorithm
needs at most $\tilde{O}(\sqrt{n})$ times as many samples needed as the best
possible algorithm for that instance (in contrast, all previous known
algorithms for the top-$K$ problem have competitive ratios of
$\tilde{\Omega}(n)$ or worse). We further show that this is tight: any
algorithm for the top-$K$ problem has competitive ratio at least
$\tilde{\Omega}(\sqrt{n})$.
| Xi Chen, Sivakanth Gopi, Jieming Mao, Jon Schneider | null | 1605.03933 | null | null |
Transfer Hashing with Privileged Information | cs.LG stat.ML | Most existing learning to hash methods assume that there are sufficient data,
either labeled or unlabeled, on the domain of interest (i.e., the target
domain) for training. However, this assumption cannot be satisfied in some
real-world applications. To address this data sparsity issue in hashing,
inspired by transfer learning, we propose a new framework named Transfer
Hashing with Privileged Information (THPI). Specifically, we extend the
standard learning to hash method, Iterative Quantization (ITQ), in a transfer
learning manner, namely ITQ+. In ITQ+, a new slack function is learned from
auxiliary data to approximate the quantization error in ITQ. We developed an
alternating optimization approach to solve the resultant optimization problem
for ITQ+. We further extend ITQ+ to LapITQ+ by utilizing the geometry structure
among the auxiliary data for learning more precise binary codes in the target
domain. Extensive experiments on several benchmark datasets verify the
effectiveness of our proposed approaches through comparisons with several
state-of-the-art baselines.
| Joey Tianyi Zhou, Xinxing Xu, Sinno Jialin Pan, Ivor W. Tsang, Zheng
Qin and Rick Siow Mong Goh | null | 1605.04034 | null | null |
Causal Discovery for Manufacturing Domains | cs.LG cs.AI | Yield and quality improvement is of paramount importance to any manufacturing
company. One of the ways of improving yield is through discovery of the root
causal factors affecting yield. We propose the use of data-driven interpretable
causal models to identify key factors affecting yield. We focus on factors that
are measured in different stages of production and testing in the manufacturing
cycle of a product. We apply causal structure learning techniques on real data
collected from this line. Specifically, the goal of this work is to learn
interpretable causal models from observational data produced by manufacturing
lines.
Emphasis has been given to the interpretability of the models to make them
actionable in the field of manufacturing. We highlight the challenges presented
by assembly line data and propose ways to alleviate them.We also identify
unique characteristics of data originating from assembly lines and how to
leverage them in order to improve causal discovery. Standard evaluation
techniques for causal structure learning shows that the learned causal models
seem to closely represent the underlying latent causal relationship between
different factors in the production process. These results were also validated
by manufacturing domain experts who found them promising. This work
demonstrates how data mining and knowledge discovery can be used for root cause
analysis in the domain of manufacturing and connected industry.
| Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade, David Jensen | null | 1605.04056 | null | null |
A Reinforcement Learning System to Encourage Physical Activity in
Diabetes Patients | cs.CY cs.LG | Regular physical activity is known to be beneficial to people suffering from
diabetes type 2. Nevertheless, most such people are sedentary. Smartphones
create new possibilities for helping people to adhere to their physical
activity goals, through continuous monitoring and communication, coupled with
personalized feedback.
We provided 27 sedentary diabetes type 2 patients with a smartphone-based
pedometer and a personal plan for physical activity. Patients were sent SMS
messages to encourage physical activity between once a day and once per week.
Messages were personalized through a Reinforcement Learning (RL) algorithm
which optimized messages to improve each participant's compliance with the
activity regimen. The RL algorithm was compared to a static policy for sending
messages and to weekly reminders.
Our results show that participants who received messages generated by the RL
algorithm increased the amount of activity and pace of walking, while the
control group patients did not. Patients assigned to the RL algorithm group
experienced a superior reduction in blood glucose levels (HbA1c) compared to
control policies, and longer participation caused greater reductions in blood
glucose levels. The learning algorithm improved gradually in predicting which
messages would lead participants to exercise.
Our results suggest that a mobile phone application coupled with a learning
algorithm can improve adherence to exercise in diabetic patients. As a learning
algorithm is automated, and delivers personalized messages, it could be used in
large populations of diabetic patients to improve health and glycemic control.
Our results can be expanded to other areas where computer-led health coaching
of humans may have a positive impact.
| Irit Hochberg and Guy Feraru and Mark Kozdoba and Shie Mannor and
Moshe Tennenholtz and Elad Yom-Tov | 10.2196/jmir.7994 | 1605.04070 | null | null |
Barzilai-Borwein Step Size for Stochastic Gradient Descent | math.OC cs.LG stat.ML | One of the major issues in stochastic gradient descent (SGD) methods is how
to choose an appropriate step size while running the algorithm. Since the
traditional line search technique does not apply for stochastic optimization
algorithms, the common practice in SGD is either to use a diminishing step
size, or to tune a fixed step size by hand, which can be time consuming in
practice. In this paper, we propose to use the Barzilai-Borwein (BB) method to
automatically compute step sizes for SGD and its variant: stochastic variance
reduced gradient (SVRG) method, which leads to two algorithms: SGD-BB and
SVRG-BB. We prove that SVRG-BB converges linearly for strongly convex objective
functions. As a by-product, we prove the linear convergence result of SVRG with
Option I proposed in [10], whose convergence result is missing in the
literature. Numerical experiments on standard data sets show that the
performance of SGD-BB and SVRG-BB is comparable to and sometimes even better
than SGD and SVRG with best-tuned step sizes, and is superior to some advanced
SGD variants.
| Conghui Tan, Shiqian Ma, Yu-Hong Dai, Yuqiu Qian | null | 1605.04131 | null | null |
Online Optimization Methods for the Quantification Problem | stat.ML cs.AI cs.IR cs.LG | The estimation of class prevalence, i.e., the fraction of a population that
belongs to a certain class, is a very useful tool in data analytics and
learning, and finds applications in many domains such as sentiment analysis,
epidemiology, etc. For example, in sentiment analysis, the objective is often
not to estimate whether a specific text conveys a positive or a negative
sentiment, but rather estimate the overall distribution of positive and
negative sentiments during an event window. A popular way of performing the
above task, often dubbed quantification, is to use supervised learning to train
a prevalence estimator from labeled data.
Contemporary literature cites several performance measures used to measure
the success of such prevalence estimators. In this paper we propose the first
online stochastic algorithms for directly optimizing these
quantification-specific performance measures. We also provide algorithms that
optimize hybrid performance measures that seek to balance quantification and
classification performance. Our algorithms present a significant advancement in
the theory of multivariate optimization and we show, by a rigorous theoretical
analysis, that they exhibit optimal convergence. We also report extensive
experiments on benchmark and real data sets which demonstrate that our methods
significantly outperform existing optimization techniques used for these
performance measures.
| Purushottam Kar and Shuai Li and Harikrishna Narasimhan and Sanjay
Chawla and Fabrizio Sebastiani | 10.1145/2939672.2939832 | 1605.04135 | null | null |
Support Vector Algorithms for Optimizing the Partial Area Under the ROC
Curve | cs.LG stat.ML | The area under the ROC curve (AUC) is a widely used performance measure in
machine learning. Increasingly, however, in several applications, ranging from
ranking to biometric screening to medicine, performance is measured not in
terms of the full area under the ROC curve, but in terms of the \emph{partial}
area under the ROC curve between two false positive rates. In this paper, we
develop support vector algorithms for directly optimizing the partial AUC
between any two false positive rates. Our methods are based on minimizing a
suitable proxy or surrogate objective for the partial AUC error. In the case of
the full AUC, one can readily construct and optimize convex surrogates by
expressing the performance measure as a summation of pairwise terms. The
partial AUC, on the other hand, does not admit such a simple decomposable
structure, making it more challenging to design and optimize (tight) convex
surrogates for this measure.
Our approach builds on the structural SVM framework of Joachims (2005) to
design convex surrogates for partial AUC, and solves the resulting optimization
problem using a cutting plane solver. Unlike the full AUC, where the
combinatorial optimization needed in each iteration of the cutting plane solver
can be decomposed and solved efficiently, the corresponding problem for the
partial AUC is harder to decompose. One of our main contributions is a
polynomial time algorithm for solving the combinatorial optimization problem
associated with partial AUC. We also develop an approach for optimizing a
tighter non-convex hinge loss based surrogate for the partial AUC using
difference-of-convex programming. Our experiments on a variety of real-world
and benchmark tasks confirm the efficacy of the proposed methods.
| Harikrishna Narasimhan, Shivani Agarwal | null | 1605.04337 | null | null |
Monotone Retargeting for Unsupervised Rank Aggregation with Object
Features | stat.ML cs.AI cs.LG | Learning the true ordering between objects by aggregating a set of expert
opinion rank order lists is an important and ubiquitous problem in many
applications ranging from social choice theory to natural language processing
and search aggregation. We study the problem of unsupervised rank aggregation
where no ground truth ordering information in available, neither about the true
preference ordering between any set of objects nor about the quality of
individual rank lists. Aggregating the often inconsistent and poor quality rank
lists in such an unsupervised manner is a highly challenging problem, and
standard consensus-based methods are often ill-defined, and difficult to solve.
In this manuscript we propose a novel framework to bypass these issues by using
object attributes to augment the standard rank aggregation framework. We design
algorithms that learn joint models on both rank lists and object features to
obtain an aggregated rank ordering that is more accurate and robust, and also
helps weed out rank lists of dubious validity. We validate our techniques on
synthetic datasets where our algorithm is able to estimate the true rank
ordering even when the rank lists are corrupted. Experiments on three real
datasets, MQ2008, MQ2008 and OHSUMED, show that using object features can
result in significant improvement in performance over existing rank aggregation
methods that do not use object information. Furthermore, when at least some of
the rank lists are of high quality, our methods are able to effectively exploit
their high expertise to output an aggregated rank ordering of great accuracy.
| Avradeep Bhowmik, Joydeep Ghosh | null | 1605.04465 | null | null |
Generalized Linear Models for Aggregated Data | stat.ML cs.AI cs.LG | Databases in domains such as healthcare are routinely released to the public
in aggregated form. Unfortunately, naive modeling with aggregated data may
significantly diminish the accuracy of inferences at the individual level. This
paper addresses the scenario where features are provided at the individual
level, but the target variables are only available as histogram aggregates or
order statistics. We consider a limiting case of generalized linear modeling
when the target variables are only known up to permutation, and explore how
this relates to permutation testing; a standard technique for assessing
statistical dependency. Based on this relationship, we propose a simple
algorithm to estimate the model parameters and individual level inferences via
alternating imputation and standard generalized linear model fitting. Our
results suggest the effectiveness of the proposed approach when, in the
original data, permutation testing accurately ascertains the veracity of the
linear relationship. The framework is extended to general histogram data with
larger bins - with order statistics such as the median as a limiting case. Our
experimental results on simulated data and aggregated healthcare data suggest a
diminishing returns property with respect to the granularity of the histogram -
when a linear relationship holds in the original data, the targets can be
predicted accurately given relatively coarse histograms.
| Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo | null | 1605.04466 | null | null |
DeepLearningKit - an GPU Optimized Deep Learning Framework for Apple's
iOS, OS X and tvOS developed in Metal and Swift | cs.LG cs.DC cs.NE | In this paper we present DeepLearningKit - an open source framework that
supports using pretrained deep learning models (convolutional neural networks)
for iOS, OS X and tvOS. DeepLearningKit is developed in Metal in order to
utilize the GPU efficiently and Swift for integration with applications, e.g.
iOS-based mobile apps on iPhone/iPad, tvOS-based apps for the big screen, or OS
X desktop applications. The goal is to support using deep learning models
trained with popular frameworks such as Caffe, Torch, TensorFlow, Theano,
Pylearn, Deeplearning4J and Mocha. Given the massive GPU resources and time
required to train Deep Learning models we suggest an App Store like model to
distribute and download pretrained and reusable Deep Learning models.
| Amund Tveit, Torbj{\o}rn Morland and Thomas Brox R{\o}st | null | 1605.04614 | null | null |
Learning to Rank Personalized Search Results in Professional Networks | cs.IR cs.LG | LinkedIn search is deeply personalized - for the same queries, different
searchers expect completely different results. This paper presents our approach
to achieving this by mining various data sources available in LinkedIn to infer
searchers' intents (such as hiring, job seeking, etc.), as well as extending
the concept of homophily to capture the searcher-result similarities on many
aspects. Then, learning-to-rank (LTR) is applied to combine these signals with
standard search features.
| Viet Ha-Thuc and Shakti Sinha | null | 1605.04624 | null | null |
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online
Learning with True and Noisy Gradient | cs.LG math.OC stat.ML | This work focuses on dynamic regret of online convex optimization that
compares the performance of online learning to a clairvoyant who knows the
sequence of loss functions in advance and hence selects the minimizer of the
loss function at each step. By assuming that the clairvoyant moves slowly
(i.e., the minimizers change slowly), we present several improved
variation-based upper bounds of the dynamic regret under the true and noisy
gradient feedback, which are {\it optimal} in light of the presented lower
bounds. The key to our analysis is to explore a regularity metric that measures
the temporal changes in the clairvoyant's minimizers, to which we refer as {\it
path variation}. Firstly, we present a general lower bound in terms of the path
variation, and then show that under full information or gradient feedback we
are able to achieve an optimal dynamic regret. Secondly, we present a lower
bound with noisy gradient feedback and then show that we can achieve optimal
dynamic regrets under a stochastic gradient feedback and two-point bandit
feedback. Moreover, for a sequence of smooth loss functions that admit a small
variation in the gradients, our dynamic regret under the two-point bandit
feedback matches what is achieved with full information.
| Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi | null | 1605.04638 | null | null |
Alternating optimization method based on nonnegative matrix
factorizations for deep neural networks | cs.LG cs.NE stat.ML | The backpropagation algorithm for calculating gradients has been widely used
in computation of weights for deep neural networks (DNNs). This method requires
derivatives of objective functions and has some difficulties finding
appropriate parameters such as learning rate. In this paper, we propose a novel
approach for computing weight matrices of fully-connected DNNs by using two
types of semi-nonnegative matrix factorizations (semi-NMFs). In this method,
optimization processes are performed by calculating weight matrices
alternately, and backpropagation (BP) is not used. We also present a method to
calculate stacked autoencoder using a NMF. The output results of the
autoencoder are used as pre-training data for DNNs. The experimental results
show that our method using three types of NMFs attains similar error rates to
the conventional DNNs with BP.
| Tetsuya Sakurai, Akira Imakura, Yuto Inoue and Yasunori Futamura | null | 1605.04639 | null | null |
Fast and Accurate Performance Analysis of LTE Radio Access Networks | cs.DC cs.LG cs.NI | An increasing amount of analytics is performed on data that is procured in a
real-time fashion to make real-time decisions. Such tasks include simple
reporting on streams to sophisticated model building. However, the practicality
of such analyses are impeded in several domains because they are faced with a
fundamental trade-off between data collection latency and analysis accuracy.
In this paper, we study this trade-off in the context of a specific domain,
Cellular Radio Access Networks (RAN). Our choice of this domain is influenced
by its commonalities with several other domains that produce real-time data,
our access to a large live dataset, and their real-time nature and
dimensionality which makes it a natural fit for a popular analysis technique,
machine learning (ML). We find that the latency accuracy trade-off can be
resolved using two broad, general techniques: intelligent data grouping and
task formulations that leverage domain characteristics. Based on this, we
present CellScope, a system that addresses this challenge by applying a domain
specific formulation and application of Multi-task Learning (MTL) to RAN
performance analysis. It achieves this goal using three techniques: feature
engineering to transform raw data into effective features, a PCA inspired
similarity metric to group data from geographically nearby base stations
sharing performance commonalities, and a hybrid online-offline model for
efficient model updates. Our evaluation of CellScope shows that its accuracy
improvements over direct application of ML range from 2.5x to 4.4x while
reducing the model update overhead by up to 4.8x. We have also used CellScope
to analyze a live LTE consisting of over 2 million subscribers for a period of
over 10 months, where it uncovered several problems and insights, some of them
previously unknown.
| Anand Padmanabha Iyer, Ion Stoica, Mosharaf Chowdhury, Li Erran Li | null | 1605.04652 | null | null |
Joint Learning of Sentence Embeddings for Relevance and Entailment | cs.CL cs.LG cs.NE | We consider the problem of Recognizing Textual Entailment within an
Information Retrieval context, where we must simultaneously determine the
relevancy as well as degree of entailment for individual pieces of evidence to
determine a yes/no answer to a binary natural language question.
We compare several variants of neural networks for sentence embeddings in a
setting of decision-making based on evidence of varying relevance. We propose a
basic model to integrate evidence for entailment, show that joint training of
the sentence embeddings to model relevance and entailment is feasible even with
no explicit per-evidence supervision, and show the importance of evaluating
strong baselines. We also demonstrate the benefit of carrying over text
comprehension model trained on an unrelated task for our small datasets.
Our research is motivated primarily by a new open dataset we introduce,
consisting of binary questions and news-based evidence snippets. We also apply
the proposed relevance-entailment model on a similar task of ranking
multiple-choice test answers, evaluating it on a preliminary dataset of school
test questions as well as the standard MCTest dataset, where we improve the
neural model state-of-art.
| Petr Baudis, Silvestr Stanko and Jan Sedivy | null | 1605.04655 | null | null |
Solve-Select-Scale: A Three Step Process For Sparse Signal Estimation | cs.IT cs.LG math.IT stat.ML | In the theory of compressed sensing (CS), the sparsity $\|x\|_0$ of the
unknown signal $\mathbf{x} \in \mathcal{R}^n$ is of prime importance and the
focus of reconstruction algorithms has mainly been either $\|x\|_0$ or its
convex relaxation (via $\|x\|_1$). However, it is typically unknown in practice
and has remained a challenge when nothing about the size of the support is
known. As pointed recently, $\|x\|_0$ might not be the best metric to minimize
directly, both due to its inherent complexity as well as its noise performance.
Recently a novel stable measure of sparsity $s(\mathbf{x}) :=
\|\mathbf{x}\|_1^2/\|\mathbf{x}\|_2^2$ has been investigated by Lopes
\cite{Lopes2012}, which is a sharp lower bound on $\|\mathbf{x}\|_0$. The
estimation procedure for this measure uses only a small number of linear
measurements, does not rely on any sparsity assumptions, and requires very
little computation. The usage of the quantity $s(\mathbf{x})$ in sparse signal
estimation problems has not received much importance yet. We develop the idea
of incorporating $s(\mathbf{x})$ into the signal estimation framework. We also
provide a three step algorithm to solve problems of the form $\mathbf{Ax=b}$
with no additional assumptions on the original signal $\mathbf{x}$.
| Mithun Das Gupta | null | 1605.04657 | null | null |
A Critical Examination of RESCAL for Completion of Knowledge Bases with
Transitive Relations | stat.ML cs.AI cs.DB cs.LG | Link prediction in large knowledge graphs has received a lot of attention
recently because of its importance for inferring missing relations and for
completing and improving noisily extracted knowledge graphs. Over the years a
number of machine learning researchers have presented various models for
predicting the presence of missing relations in a knowledge base. Although all
the previous methods are presented with empirical results that show high
performance on select datasets, there is almost no previous work on
understanding the connection between properties of a knowledge base and the
performance of a model. In this paper we analyze the RESCAL method and prove
that it can not encode asymmetric transitive relations in knowledge bases.
| Pushpendre Rastogi, Benjamin Van Durme | null | 1605.04672 | null | null |
CNN based texture synthesize with Semantic segment | cs.CV cs.GR cs.LG | Deep learning algorithm display powerful ability in Computer Vision area, in
recent year, the CNN has been applied to solve problems in the subarea of
Image-generating, which has been widely applied in areas such as photo editing,
image design, computer animation, real-time rendering for large scale of scenes
and for visual effects in movies. However in the texture synthesize procedure.
The state-of-art CNN can not capture the spatial location of texture in image,
lead to significant distortion after texture synthesize, we propose a new way
to generating-image by adding the semantic segment step with deep learning
algorithm as Pre-Processing and analyze the outcome.
| Xianye Liang, Bocheng Zhuo, Peijie Li, Liangju He | null | 1605.04731 | null | null |
Geometry Aware Mappings for High Dimensional Sparse Factors | cs.LG cs.IR stat.ML | While matrix factorisation models are ubiquitous in large scale
recommendation and search, real time application of such models requires inner
product computations over an intractably large set of item factors. In this
manuscript we present a novel framework that uses the inverted index
representation to exploit structural properties of sparse vectors to
significantly reduce the run time computational cost of factorisation models.
We develop techniques that use geometry aware permutation maps on a tessellated
unit sphere to obtain high dimensional sparse embeddings for latent factors
with sparsity patterns related to angular closeness of the original latent
factors. We also design several efficient and deterministic realisations within
this framework and demonstrate with experiments that our techniques lead to
faster run time operation with minimal loss of accuracy.
| Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Narayan Bhaskar,
Suju Rajan | null | 1605.04764 | null | null |
Off-policy evaluation for slate recommendation | cs.LG cs.AI stat.ML | This paper studies the evaluation of policies that recommend an ordered set
of items (e.g., a ranking) based on some context---a common scenario in web
search, ads, and recommendation. We build on techniques from combinatorial
bandits to introduce a new practical estimator that uses logged data to
estimate a policy's performance. A thorough empirical evaluation on real-world
data reveals that our estimator is accurate in a variety of settings, including
as a subroutine in a learning-to-rank task, where it achieves competitive
performance. We derive conditions under which our estimator is unbiased---these
conditions are weaker than prior heuristics for slate evaluation---and
experimentally demonstrate a smaller bias than parametric approaches, even when
these conditions are violated. Finally, our theory and experiments also show
exponential savings in the amount of required data compared with general
unbiased estimators.
| Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav
Dud\'ik, John Langford, Damien Jose, Imed Zitouni | null | 1605.04812 | null | null |
Reducing the Model Order of Deep Neural Networks Using Information
Theory | cs.LG cs.NE | Deep neural networks are typically represented by a much larger number of
parameters than shallow models, making them prohibitive for small footprint
devices. Recent research shows that there is considerable redundancy in the
parameter space of deep neural networks. In this paper, we propose a method to
compress deep neural networks by using the Fisher Information metric, which we
estimate through a stochastic optimization method that keeps track of
second-order information in the network. We first remove unimportant parameters
and then use non-uniform fixed point quantization to assign more bits to
parameters with higher Fisher Information estimates. We evaluate our method on
a classification task with a convolutional neural network trained on the MNIST
data set. Experimental results show that our method outperforms existing
methods for both network pruning and quantization.
| Ming Tu, Visar Berisha, Yu Cao, Jae-sun Seo | null | 1605.04859 | null | null |
Gearbox Fault Detection through PSO Exact Wavelet Analysis and SVM
Classifier | cs.LG cs.AI cs.CV | Time-frequency methods for vibration-based gearbox faults detection have been
considered the most efficient method. Among these methods, continuous wavelet
transform (CWT) as one of the best time-frequency method has been used for both
stationary and transitory signals. Some deficiencies of CWT are problem of
overlapping and distortion ofsignals. In this condition, a large amount of
redundant information exists so that it may cause false alarm or
misinterpretation of the operator. In this paper a modified method called Exact
Wavelet Analysis is used to minimize the effects of overlapping and distortion
in case of gearbox faults. To implement exact wavelet analysis, Particle Swarm
Optimization (PSO) algorithm has been used for this purpose. This method have
been implemented for the acceleration signals from 2D acceleration sensor
acquired by Advantech PCI-1710 card from a gearbox test setup in Amirkabir
University of Technology. Gearbox has been considered in both healthy and
chipped tooth gears conditions. Kernelized Support Vector Machine (SVM) with
radial basis functions has used the extracted features from exact wavelet
analysis for classification. The efficiency of this classifier is then
evaluated with the other signals acquired from the setup test. The results show
that in comparison of CWT, PSO Exact Wavelet Transform has better ability in
feature extraction in price of more computational effort. In addition, PSO
exact wavelet has better speed comparing to Genetic Algorithm (GA) exact
wavelet in condition of equal population because of factoring mutation and
crossover in PSO algorithm. SVM classifier with the extracted features in
gearbox shows very good results and its ability has been proved.
| Amir Hosein Zamanian, Abdolreza Ohadi | 10.13140/RG.2.1.4983.3442 | 1605.04874 | null | null |
A Constant-Factor Bi-Criteria Approximation Guarantee for $k$-means++ | cs.LG cs.CG | This paper studies the $k$-means++ algorithm for clustering as well as the
class of $D^\ell$ sampling algorithms to which $k$-means++ belongs. It is shown
that for any constant factor $\beta > 1$, selecting $\beta k$ cluster centers
by $D^\ell$ sampling yields a constant-factor approximation to the optimal
clustering with $k$ centers, in expectation and without conditions on the
dataset. This result extends the previously known $O(\log k)$ guarantee for the
case $\beta = 1$ to the constant-factor bi-criteria regime. It also improves
upon an existing constant-factor bi-criteria result that holds only with
constant probability.
| Dennis Wei | null | 1605.04986 | null | null |
Locally Weighted Ensemble Clustering | cs.LG | Due to its ability to combine multiple base clusterings into a probably
better and more robust clustering, the ensemble clustering technique has been
attracting increasing attention in recent years. Despite the significant
success, one limitation to most of the existing ensemble clustering methods is
that they generally treat all base clusterings equally regardless of their
reliability, which makes them vulnerable to low-quality base clusterings.
Although some efforts have been made to (globally) evaluate and weight the base
clusterings, yet these methods tend to view each base clustering as an
individual and neglect the local diversity of clusters inside the same base
clustering. It remains an open problem how to evaluate the reliability of
clusters and exploit the local diversity in the ensemble to enhance the
consensus performance, especially in the case when there is no access to data
features or specific assumptions on data distribution. To address this, in this
paper, we propose a novel ensemble clustering approach based on ensemble-driven
cluster uncertainty estimation and local weighting strategy. In particular, the
uncertainty of each cluster is estimated by considering the cluster labels in
the entire ensemble via an entropic criterion. A novel ensemble-driven cluster
validity measure is introduced, and a locally weighted co-association matrix is
presented to serve as a summary for the ensemble of diverse clusters. With the
local diversity in ensembles exploited, two novel consensus functions are
further proposed. Extensive experiments on a variety of real-world datasets
demonstrate the superiority of the proposed approach over the state-of-the-art.
| Dong Huang, Chang-Dong Wang, Jian-Huang Lai | 10.1109/TCYB.2017.2702343 | 1605.05011 | null | null |
Incremental Robot Learning of New Objects with Fixed Update Time | stat.ML cs.CV cs.LG cs.RO | We consider object recognition in the context of lifelong learning, where a
robotic agent learns to discriminate between a growing number of object classes
as it accumulates experience about the environment. We propose an incremental
variant of the Regularized Least Squares for Classification (RLSC) algorithm,
and exploit its structure to seamlessly add new classes to the learned model.
The presented algorithm addresses the problem of having an unbalanced
proportion of training examples per class, which occurs when new objects are
presented to the system for the first time.
We evaluate our algorithm on both a machine learning benchmark dataset and
two challenging object recognition tasks in a robotic setting. Empirical
evidence shows that our approach achieves comparable or higher classification
performance than its batch counterpart when classes are unbalanced, while being
significantly faster.
| Raffaello Camoriano, Giulia Pasquale, Carlo Ciliberto, Lorenzo Natale,
Lorenzo Rosasco, Giorgio Metta | null | 1605.05045 | null | null |
Word2Vec is a special case of Kernel Correspondence Analysis and Kernels
for Natural Language Processing | cs.LG cs.CL | We show that correspondence analysis (CA) is equivalent to defining a Gini
index with appropriately scaled one-hot encoding. Using this relation, we
introduce a nonlinear kernel extension to CA. This extended CA gives a known
analysis for natural language via specialized kernels that use an appropriate
contingency table. We propose a semi-supervised CA, which is a special case of
the kernel extension to CA. Because CA requires excessive memory if applied to
numerous categories, CA has not been used for natural language processing. We
address this problem by introducing delayed evaluation to randomized singular
value decomposition. The memory-efficient CA is then applied to a word-vector
representation task. We propose a tail-cut kernel, which is an extension to the
skip-gram within the kernel extension to CA. Our tail-cut kernel outperforms
existing word-vector representation methods.
| Hirotaka Niitsuma and Minho Lee | null | 1605.05087 | null | null |
Automatic Classification of Irregularly Sampled Time Series with Unequal
Lengths: A Case Study on Estimated Glomerular Filtration Rate | cs.LG cs.CE | A patient's estimated glomerular filtration rate (eGFR) can provide important
information about disease progression and kidney function. Traditionally, an
eGFR time series is interpreted by a human expert labelling it as stable or
unstable. While this approach works for individual patients, the time consuming
nature of it precludes the quick evaluation of risk in large numbers of
patients. However, automating this process poses significant challenges as eGFR
measurements are usually recorded at irregular intervals and the series of
measurements differs in length between patients. Here we present a two-tier
system to automatically classify an eGFR trend. First, we model the time series
using Gaussian process regression (GPR) to fill in `gaps' by resampling a fixed
size vector of fifty time-dependent observations. Second, we classify the
resampled eGFR time series using a K-NN/SVM classifier, and evaluate its
performance via 5-fold cross validation. Using this approach we achieved an
F-score of 0.90, compared to 0.96 for 5 human experts when scored amongst
themselves.
| Santosh Tirunagari, Simon Bull and Norman Poh | null | 1605.05142 | null | null |
Multimodal Sparse Coding for Event Detection | cs.LG cs.CV | Unsupervised feature learning methods have proven effective for
classification tasks based on a single modality. We present multimodal sparse
coding for learning feature representations shared across multiple modalities.
The shared representations are applied to multimedia event detection (MED) and
evaluated in comparison to unimodal counterparts, as well as other feature
learning methods such as GMM supervectors and sparse RBM. We report the
cross-validated classification accuracy and mean average precision of the MED
system trained on features learned from our unimodal and multimodal settings
for a subset of the TRECVID MED 2014 dataset.
| Youngjune Gwon and William Campbell and Kevin Brady and Douglas Sturim
and Miriam Cha and H.T. Kung | null | 1605.05212 | null | null |
On the boosting ability of top-down decision tree learning algorithm for
multiclass classification | cs.LG | We analyze the performance of the top-down multiclass classification
algorithm for decision tree learning called LOMtree, recently proposed in the
literature Choromanska and Langford (2014) for solving efficiently
classification problems with very large number of classes. The algorithm online
optimizes the objective function which simultaneously controls the depth of the
tree and its statistical accuracy. We prove important properties of this
objective and explore its connection to three well-known entropy-based decision
tree objectives, i.e. Shannon entropy, Gini-entropy and its modified version,
for which instead online optimization schemes were not yet developed. We show,
via boosting-type guarantees, that maximizing the considered objective leads
also to the reduction of all of these entropy-based objectives. The bounds we
obtain critically depend on the strong-concavity properties of the
entropy-based criteria, where the mildest dependence on the number of classes
(only logarithmic) corresponds to the Shannon entropy.
| Anna Choromanska and Krzysztof Choromanski and Mariusz Bojarski | null | 1605.05223 | null | null |
Biologically Inspired Radio Signal Feature Extraction with Sparse
Denoising Autoencoders | stat.ML cs.LG cs.NE | Automatic modulation classification (AMC) is an important task for modern
communication systems; however, it is a challenging problem when signal
features and precise models for generating each modulation may be unknown. We
present a new biologically-inspired AMC method without the need for models or
manually specified features --- thus removing the requirement for expert prior
knowledge. We accomplish this task using regularized stacked sparse denoising
autoencoders (SSDAs). Our method selects efficient classification features
directly from raw in-phase/quadrature (I/Q) radio signals in an unsupervised
manner. These features are then used to construct higher-complexity abstract
features which can be used for automatic modulation classification. We
demonstrate this process using a dataset generated with a software defined
radio, consisting of random input bits encoded in 100-sample segments of
various common digital radio modulations. Our results show correct
classification rates of > 99% at 7.5 dB signal-to-noise ratio (SNR) and > 92%
at 0 dB SNR in a 6-way classification test. Our experiments demonstrate a
dramatically new and broadly applicable mechanism for performing AMC and
related tasks without the need for expert-defined or modulation-specific signal
information.
| Benjamin Migliori, Riley Zeller-Townson, Daniel Grady, Daniel Gebhardt | null | 1605.05239 | null | null |
Learning Convolutional Neural Networks for Graphs | cs.LG cs.AI stat.ML | Numerous important problems can be framed as learning from graph data. We
propose a framework for learning convolutional neural networks for arbitrary
graphs. These graphs may be undirected, directed, and with both discrete and
continuous node and edge attributes. Analogous to image-based convolutional
networks that operate on locally connected regions of the input, we present a
general approach to extracting locally connected regions from graphs. Using
established benchmark data sets, we demonstrate that the learned feature
representations are competitive with state of the art graph kernels and that
their computation is highly efficient.
| Mathias Niepert and Mohamed Ahmed and Konstantin Kutzkov | null | 1605.05273 | null | null |
Minimax Lower Bounds for Kronecker-Structured Dictionary Learning | cs.IT cs.LG math.IT stat.ML | Dictionary learning is the problem of estimating the collection of atomic
elements that provide a sparse representation of measured/collected signals or
data. This paper finds fundamental limits on the sample complexity of
estimating dictionaries for tensor data by proving a lower bound on the minimax
risk. This lower bound depends on the dimensions of the tensor and parameters
of the generative model. The focus of this paper is on second-order tensor
data, with the underlying dictionaries constructed by taking the Kronecker
product of two smaller dictionaries and the observed data generated by sparse
linear combinations of dictionary atoms observed through white Gaussian noise.
In this regard, the paper provides a general lower bound on the minimax risk
and also adapts the proof techniques for equivalent results using sparse and
Gaussian coefficient models. The reported results suggest that the sample
complexity of dictionary learning for tensor data can be significantly lower
than that for unstructured data.
| Zahra Shakeri, Waheed U. Bajwa, Anand D. Sarwate | 10.1109/ISIT.2016.7541479 | 1605.05284 | null | null |
Option Discovery in Hierarchical Reinforcement Learning using
Spatio-Temporal Clustering | cs.LG cs.AI cs.CV cs.NE | This paper introduces an automated skill acquisition framework in
reinforcement learning which involves identifying a hierarchical description of
the given task in terms of abstract states and extended actions between
abstract states. Identifying such structures present in the task provides ways
to simplify and speed up reinforcement learning algorithms. These structures
also help to generalize such algorithms over multiple tasks without relearning
policies from scratch. We use ideas from dynamical systems to find metastable
regions in the state space and associate them with abstract states. The
spectral clustering algorithm PCCA+ is used to identify suitable abstractions
aligned to the underlying structure. Skills are defined in terms of the
sequence of actions that lead to transitions between such abstract states. The
connectivity information from PCCA+ is used to generate these skills or
options. These skills are independent of the learning task and can be
efficiently reused across a variety of tasks defined over the same model. This
approach works well even without the exact model of the environment by using
sample trajectories to construct an approximate estimate. We also present our
approach to scaling the skill acquisition framework to complex tasks with large
state spaces for which we perform state aggregation using the representation
learned from an action conditional video prediction network and use the skill
acquisition framework on the aggregated state space.
| Aravind Srinivas, Ramnandan Krishnamurthy, Peeyush Kumar and Balaraman
Ravindran | null | 1605.05359 | null | null |
Yelp Dataset Challenge: Review Rating Prediction | cs.CL cs.IR cs.LG | Review websites, such as TripAdvisor and Yelp, allow users to post online
reviews for various businesses, products and services, and have been recently
shown to have a significant influence on consumer shopping behaviour. An online
review typically consists of free-form text and a star rating out of 5. The
problem of predicting a user's star rating for a product, given the user's text
review for that product, is called Review Rating Prediction and has lately
become a popular, albeit hard, problem in machine learning. In this paper, we
treat Review Rating Prediction as a multi-class classification problem, and
build sixteen different prediction models by combining four feature extraction
methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic
Indexing, with four machine learning algorithms, (i) logistic regression, (ii)
Naive Bayes classification, (iii) perceptrons, and (iv) linear Support Vector
Classification. We analyse the performance of each of these sixteen models to
come up with the best model for predicting the ratings from reviews. We use the
dataset provided by Yelp for training and testing the models.
| Nabiha Asghar | null | 1605.05362 | null | null |
Dynamic Frame skip Deep Q Network | cs.LG cs.AI cs.NE | Deep Reinforcement Learning methods have achieved state of the art
performance in learning control policies for the games in the Atari 2600
domain. One of the important parameters in the Arcade Learning Environment
(ALE) is the frame skip rate. It decides the granularity at which agents can
control game play. A frame skip value of $k$ allows the agent to repeat a
selected action $k$ number of times. The current state of the art architectures
like Deep Q-Network (DQN) and Dueling Network Architectures (DuDQN) consist of
a framework with a static frame skip rate, where the action output from the
network is repeated for a fixed number of frames regardless of the current
state. In this paper, we propose a new architecture, Dynamic Frame skip Deep
Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable
parameter. This allows us to choose the number of times an action is to be
repeated based on the current state. We show empirically that such a setting
improves the performance on relatively harder games like Seaquest.
| Aravind Srinivas, Sahil Sharma and Balaraman Ravindran | null | 1605.05365 | null | null |
Deep Action Sequence Learning for Causal Shape Transformation | cs.LG cs.CV cs.NE | Deep learning became the method of choice in recent year for solving a wide
variety of predictive analytics tasks. For sequence prediction, recurrent
neural networks (RNN) are often the go-to architecture for exploiting
sequential information where the output is dependent on previous computation.
However, the dependencies of the computation lie in the latent domain which may
not be suitable for certain applications involving the prediction of a
step-wise transformation sequence that is dependent on the previous computation
only in the visible domain. We propose that a hybrid architecture of
convolution neural networks (CNN) and stacked autoencoders (SAE) is sufficient
to learn a sequence of actions that nonlinearly transforms an input shape or
distribution into a target shape or distribution with the same support. While
such a framework can be useful in a variety of problems such as robotic path
planning, sequential decision-making in games, and identifying material
processing pathways to achieve desired microstructures, the application of the
framework is exemplified by the control of fluid deformations in a microfluidic
channel by deliberately placing a sequence of pillars. Learning of a multistep
topological transform has significant implications for rapid advances in
material science and biomedical applications.
| Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar
Ganapathysubramanian, Soumik Sarkar | null | 1605.05368 | null | null |
On the Evaluation of Dialogue Systems with Next Utterance Classification | cs.CL cs.LG | An open challenge in constructing dialogue systems is developing methods for
automatically learning dialogue strategies from large amounts of unlabelled
data. Recent work has proposed Next-Utterance-Classification (NUC) as a
surrogate task for building dialogue systems from text data. In this paper we
investigate the performance of humans on this task to validate the relevance of
NUC as a method of evaluation. Our results show three main findings: (1) humans
are able to correctly classify responses at a rate much better than chance,
thus confirming that the task is feasible, (2) human performance levels vary
across task domains (we consider 3 datasets) and expertise levels (novice vs
experts), thus showing that a range of performance is possible on this type of
task, (3) automated dialogue systems built using state-of-the-art machine
learning methods have similar performance to the human novices, but worse than
the experts, thus confirming the utility of this class of tasks for driving
further research in automated dialogue systems.
| Ryan Lowe, Iulian V. Serban, Mike Noseworthy, Laurent Charlin, Joelle
Pineau | null | 1605.05414 | null | null |
Optimization Beyond Prediction: Prescriptive Price Optimization | math.OC cs.LG stat.ML | This paper addresses a novel data science problem, prescriptive price
optimization, which derives the optimal price strategy to maximize future
profit/revenue on the basis of massive predictive formulas produced by machine
learning. The prescriptive price optimization first builds sales forecast
formulas of multiple products, on the basis of historical data, which reveal
complex relationships between sales and prices, such as price elasticity of
demand and cannibalization. Then, it constructs a mathematical optimization
problem on the basis of those predictive formulas. We present that the
optimization problem can be formulated as an instance of binary quadratic
programming (BQP). Although BQP problems are NP-hard in general and
computationally intractable, we propose a fast approximation algorithm using a
semi-definite programming (SDP) relaxation, which is closely related to the
Goemans-Williamson's Max-Cut approximation. Our experiments on simulation and
real retail datasets show that our prescriptive price optimization
simultaneously derives the optimal prices of tens/hundreds products with
practical computational time, that potentially improve 8.2% of gross profit of
those products.
| Shinji Ito and Ryohei Fujimaki | null | 1605.05422 | null | null |
Learning activation functions from data using cubic spline interpolation | stat.ML cs.LG cs.NE | Neural networks require a careful design in order to perform properly on a
given task. In particular, selecting a good activation function (possibly in a
data-dependent fashion) is a crucial step, which remains an open problem in the
research community. Despite a large amount of investigations, most current
implementations simply select one fixed function from a small set of
candidates, which is not adapted during training, and is shared among all
neurons throughout the different layers. However, neither two of these
assumptions can be supposed optimal in practice. In this paper, we present a
principled way to have data-dependent adaptation of the activation functions,
which is performed independently for each neuron. This is achieved by
leveraging over past and present advances on cubic spline interpolation,
allowing for local adaptation of the functions around their regions of use. The
resulting algorithm is relatively cheap to implement, and overfitting is
counterbalanced by the inclusion of a novel damping criterion, which penalizes
unwanted oscillations from a predefined shape. Experimental results validate
the proposal over two well-known benchmarks.
| Simone Scardapane, Michele Scarpiniti, Danilo Comminiello, Aurelio
Uncini | 10.1007/978-3-319-95098-3_7 | 1605.05509 | null | null |
Detecting Novel Processes with CANDIES -- An Holistic Novelty Detection
Technique based on Probabilistic Models | cs.LG | In this article, we propose CANDIES (Combined Approach for Novelty Detection
in Intelligent Embedded Systems), a new approach to novelty detection in
technical systems. We assume that in a technical system several processes
interact. If we observe these processes with sensors, we are able to model the
observations (samples) with a probabilistic model, where, in an ideal case, the
components of the parametric mixture density model we use, correspond to the
processes in the real world. Eventually, at run-time, novel processes emerge in
the technical systems such as in the case of an unpredictable failure. As a
consequence, new kinds of samples are observed that require an adaptation of
the model. CANDIES relies on mixtures of Gaussians which can be used for
classification purposes, too. New processes may emerge in regions of the
models' input spaces where few samples were observed before (low-density
regions) or in regions where already many samples were available (high-density
regions). The latter case is more difficult, but most existing solutions focus
on the former. Novelty detection in low- and high-density regions requires
different detection strategies. With CANDIES, we introduce a new technique to
detect novel processes in high-density regions by means of a fast online
goodness-of-fit test. For detection in low-density regions we combine this
approach with a 2SND (Two-Stage-Novelty-Detector) which we presented in
preliminary work. The properties of CANDIES are evaluated using artificial data
and benchmark data from the field of intrusion detection in computer networks,
where the task is to detect new kinds of attacks.
| Christian Gruhl, Bernhard Sick | null | 1605.05628 | null | null |
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive
Approaches | cs.LG | This paper studies graph-based active learning, where the goal is to
reconstruct a binary signal defined on the nodes of a weighted graph, by
sampling it on a small subset of the nodes. A new sampling algorithm is
proposed, which sequentially selects the graph nodes to be sampled, based on an
aggressive search for the boundary of the signal over the graph. The algorithm
generalizes a recent method for sampling nodes in unweighted graphs. The
generalization improves the sampling performance using the information gained
from the available graph weights. An analysis of the number of samples required
by the proposed algorithm is provided, and the gain over the unweighted method
is further demonstrated in simulations. Additionally, the proposed method is
compared with an alternative state of-the-art method, which is based on the
graph's spectral properties. It is shown that the proposed method significantly
outperforms the spectral sampling method, if the signal needs to be predicted
with high accuracy. On the other hand, if a higher level of inaccuracy is
tolerable, then the spectral method outperforms the proposed aggressive search
method. Consequently, we propose a hybrid method, which is shown to combine the
advantages of both approaches.
| Eyal En Gad, Akshay Gadde, A. Salman Avestimehr and Antonio Ortega | null | 1605.05710 | null | null |
Linearized GMM Kernels and Normalized Random Fourier Features | cs.LG cs.IR stat.ML | The method of "random Fourier features (RFF)" has become a popular tool for
approximating the "radial basis function (RBF)" kernel. The variance of RFF is
actually large. Interestingly, the variance can be substantially reduced by a
simple normalization step as we theoretically demonstrate. We name the improved
scheme as the "normalized RFF (NRFF)".
We also propose the "generalized min-max (GMM)" kernel as a measure of data
similarity. GMM is positive definite as there is an associated hashing method
named "generalized consistent weighted sampling (GCWS)" which linearizes this
nonlinear kernel. We provide an extensive empirical evaluation of the RBF
kernel and the GMM kernel on more than 50 publicly available datasets. For a
majority of the datasets, the (tuning-free) GMM kernel outperforms the
best-tuned RBF kernel.
We conduct extensive experiments for comparing the linearized RBF kernel
using NRFF with the linearized GMM kernel using GCWS. We observe that, to reach
a comparable classification accuracy, GCWS typically requires substantially
fewer samples than NRFF, even on datasets where the original RBF kernel
outperforms the original GMM kernel. The empirical success of GCWS (compared to
NRFF) can also be explained from a theoretical perspective. Firstly, the
relative variance (normalized by the squared expectation) of GCWS is
substantially smaller than that of NRFF, except for the very high similarity
region (where the variances of both methods are close to zero). Secondly, if we
make a model assumption on the data, we can show analytically that GCWS
exhibits much smaller variance than NRFF for estimating the same object (e.g.,
the RBF kernel), except for the very high similarity region.
| Ping Li | null | 1605.05721 | null | null |
Supervised Learning with Quantum-Inspired Tensor Networks | stat.ML cond-mat.str-el cs.LG | Tensor networks are efficient representations of high-dimensional tensors
which have been very successful for physics and mathematics applications. We
demonstrate how algorithms for optimizing such networks can be adapted to
supervised learning tasks by using matrix product states (tensor trains) to
parameterize models for classifying images. For the MNIST data set we obtain
less than 1% test set classification error. We discuss how the tensor network
form imparts additional structure to the learned model and suggest a possible
generative interpretation.
| E. Miles Stoudenmire and David J. Schwab | null | 1605.05775 | null | null |
Recurrent Exponential-Family Harmoniums without Backprop-Through-Time | cs.LG stat.ML | Exponential-family harmoniums (EFHs), which extend restricted Boltzmann
machines (RBMs) from Bernoulli random variables to other exponential families
(Welling et al., 2005), are generative models that can be trained with
unsupervised-learning techniques, like contrastive divergence (Hinton et al.
2006; Hinton, 2002), as density estimators for static data. Methods for
extending RBMs--and likewise EFHs--to data with temporal dependencies have been
proposed previously (Sutskever and Hinton, 2007; Sutskever et al., 2009), the
learning procedure being validated by qualitative assessment of the generative
model. Here we propose and justify, from a very different perspective, an
alternative training procedure, proving sufficient conditions for optimal
inference under that procedure. The resulting algorithm can be learned with
only forward passes through the data--backprop-through-time is not required, as
in previous approaches. The proof exploits a recent result about information
retention in density estimators (Makin and Sabes, 2015), and applies it to a
"recurrent EFH" (rEFH) by induction. Finally, we demonstrate optimality by
simulation, testing the rEFH: (1) as a filter on training data generated with a
linear dynamical system, the position of which is noisily reported by a
population of "neurons" with Poisson-distributed spike counts; and (2) with the
qualitative experiments proposed by Sutskever et al. (2009).
| Joseph G. Makin, Benjamin K. Dichter, Philip N. Sabes | null | 1605.05799 | null | null |
Declarative Machine Learning - A Classification of Basic Properties and
Types | cs.DB cs.DC cs.LG cs.PL | Declarative machine learning (ML) aims at the high-level specification of ML
tasks or algorithms, and automatic generation of optimized execution plans from
these specifications. The fundamental goal is to simplify the usage and/or
development of ML algorithms, which is especially important in the context of
large-scale computations. However, ML systems at different abstraction levels
have emerged over time and accordingly there has been a controversy about the
meaning of this general definition of declarative ML. Specification
alternatives range from ML algorithms expressed in domain-specific languages
(DSLs) with optimization for performance, to ML task (learning problem)
specifications with optimization for performance and accuracy. We argue that
these different types of declarative ML complement each other as they address
different users (data scientists and end users). This paper makes an attempt to
create a taxonomy for declarative ML, including a definition of essential basic
properties and types of declarative ML. Along the way, we provide insights into
implications of these properties. We also use this taxonomy to classify
existing systems. Finally, we draw conclusions on defining appropriate
benchmarks and specification languages for declarative ML.
| Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold
Reinwald | null | 1605.05826 | null | null |
A Multi-Batch L-BFGS Method for Machine Learning | math.OC cs.LG stat.ML | The question of how to parallelize the stochastic gradient descent (SGD)
method has received much attention in the literature. In this paper, we focus
instead on batch methods that use a sizeable fraction of the training set at
each iteration to facilitate parallelism, and that employ second-order
information. In order to improve the learning process, we follow a multi-batch
approach in which the batch changes at each iteration. This can cause
difficulties because L-BFGS employs gradient differences to update the Hessian
approximations, and when these gradients are computed using different data
points the process can be unstable. This paper shows how to perform stable
quasi-Newton updating in the multi-batch setting, illustrates the behavior of
the algorithm in a distributed computing platform, and studies its convergence
properties for both the convex and nonconvex cases.
| Albert S. Berahas, Jorge Nocedal, Martin Tak\'a\v{c} | null | 1605.06049 | null | null |
One-shot Learning with Memory-Augmented Neural Networks | cs.LG | Despite recent breakthroughs in the applications of deep neural networks, one
setting that presents a persistent challenge is that of "one-shot learning."
Traditional gradient-based networks require a lot of data to learn, often
through extensive iterative training. When new data is encountered, the models
must inefficiently relearn their parameters to adequately incorporate the new
information without catastrophic interference. Architectures with augmented
memory capacities, such as Neural Turing Machines (NTMs), offer the ability to
quickly encode and retrieve new information, and hence can potentially obviate
the downsides of conventional models. Here, we demonstrate the ability of a
memory-augmented neural network to rapidly assimilate new data, and leverage
this data to make accurate predictions after only a few samples. We also
introduce a new method for accessing an external memory that focuses on memory
content, unlike previous methods that additionally use memory location-based
focusing mechanisms.
| Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra,
Timothy Lillicrap | null | 1605.06065 | null | null |
A Hierarchical Latent Variable Encoder-Decoder Model for Generating
Dialogues | cs.CL cs.AI cs.LG cs.NE | Sequential data often possesses a hierarchical structure with complex
dependencies between subsequences, such as found between the utterances in a
dialogue. In an effort to model this kind of generative process, we propose a
neural network-based generative architecture, with latent stochastic variables
that span a variable number of time steps. We apply the proposed model to the
task of dialogue response generation and compare it with recent neural network
architectures. We evaluate the model performance through automatic evaluation
metrics and by carrying out a human evaluation. The experiments demonstrate
that our model improves upon recently proposed models and that the latent
variables facilitate the generation of long outputs and maintain the context.
| Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin,
Joelle Pineau, Aaron Courville, Yoshua Bengio | null | 1605.06069 | null | null |
On a convergent off -policy temporal difference learning algorithm in
on-line learning environment | cs.LG | In this paper we provide a rigorous convergence analysis of a "off"-policy
temporal difference learning algorithm with linear function approximation and
per time-step linear computational complexity in "online" learning environment.
The algorithm considered here is TDC with importance weighting introduced by
Maei et al. We support our theoretical results by providing suitable empirical
results for standard off-policy counterexamples.
| Prasenjit Karmakar, Rajkumar Maity, Shalabh Bhatnagar | null | 1605.06076 | null | null |
Inter-Battery Topic Representation Learning | cs.LG cs.CV | In this paper, we present the Inter-Battery Topic Model (IBTM). Our approach
extends traditional topic models by learning a factorized latent variable
representation. The structured representation leads to a model that marries
benefits traditionally associated with a discriminative approach, such as
feature selection, with those of a generative model, such as principled
regularization and ability to handle missing data. The factorization is
provided by representing data in terms of aligned pairs of observations as
different views. This provides means for selecting a representation that
separately models topics that exist in both views from the topics that are
unique to a single view. This structured consolidation allows for efficient and
robust inference and provides a compact and efficient representation. Learning
is performed in a Bayesian fashion by maximizing a rigorous bound on the
log-likelihood. Firstly, we illustrate the benefits of the model on a synthetic
dataset,. The model is then evaluated in both uni- and multi-modality settings
on two different classification tasks with off-the-shelf convolutional neural
network (CNN) features which generate state-of-the-art results with extremely
compact representations.
| Cheng Zhang and Hedvig Kjellstrom and Carl Henrik Ek | null | 1605.06155 | null | null |
Evaluation System for a Bayesian Optimization Service | cs.LG | Bayesian optimization is an elegant solution to the hyperparameter
optimization problem in machine learning. Building a reliable and robust
Bayesian optimization service requires careful testing methodology and sound
statistical analysis. In this talk we will outline our development of an
evaluation framework to rigorously test and measure the impact of changes to
the SigOpt optimization service. We present an overview of our evaluation
system and discuss how this framework empowers our research engineers to
confidently and quickly make changes to our core optimization engine
| Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra
Johnson, George Ke | null | 1605.06170 | null | null |
Variational hybridization and transformation for large inaccurate
noisy-or networks | cs.LG cs.AI stat.ML | Variational inference provides approximations to the computationally
intractable posterior distribution in Bayesian networks. A prominent medical
application of noisy-or Bayesian network is to infer potential diseases given
observed symptoms. Previous studies focus on approximating a handful of
complicated pathological cases using variational transformation. Our goal is to
use variational transformation as part of a novel hybridized inference for
serving reliable and real time diagnosis at web scale. We propose a hybridized
inference that allows variational parameters to be estimated without disease
posteriors or priors, making the inference faster and much of its computation
recyclable. In addition, we propose a transformation ranking algorithm that is
very stable to large variances in network prior probabilities, a common issue
that arises in medical applications of Bayesian networks. In experiments, we
perform comparative study on a large real life medical network and scalability
study on a much larger (36,000x) synthesized network.
| Yusheng Xie, Nan Du, Wei Fan, Jing Zhai, Weicheng Zhu | null | 1605.06181 | null | null |
Adversarial Delays in Online Strongly-Convex Optimization | cs.LG cs.AI stat.ML | We consider the problem of strongly-convex online optimization in presence of
adversarial delays; in a T-iteration online game, the feedback of the player's
query at time t is arbitrarily delayed by an adversary for d_t rounds and
delivered before the game ends, at iteration t+d_t-1. Specifically for
\algo{online-gradient-descent} algorithm we show it has a simple regret bound
of \Oh{\sum_{t=1}^T \log (1+ \frac{d_t}{t})}. This gives a clear and simple
bound without resorting any distributional and limiting assumptions on the
delays. We further show how this result encompasses and generalizes several of
the existing known results in the literature. Specifically it matches the
celebrated logarithmic regret \Oh{\log T} when there are no delays (i.e. d_t =
1) and regret bound of \Oh{\tau \log T} for constant delays d_t = \tau.
| Daniel Khashabi, Kent Quanrud, Amirhossein Taghvaei | null | 1605.06201 | null | null |
Faster Projection-free Convex Optimization over the Spectrahedron | math.OC cs.LG | Minimizing a convex function over the spectrahedron, i.e., the set of all
positive semidefinite matrices with unit trace, is an important optimization
task with many applications in optimization, machine learning, and signal
processing. It is also notoriously difficult to solve in large-scale since
standard techniques require expensive matrix decompositions. An alternative, is
the conditional gradient method (aka Frank-Wolfe algorithm) that regained much
interest in recent years, mostly due to its application to this specific
setting. The key benefit of the CG method is that it avoids expensive matrix
decompositions all together, and simply requires a single eigenvector
computation per iteration, which is much more efficient. On the downside, the
CG method, in general, converges with an inferior rate. The error for
minimizing a $\beta$-smooth function after $t$ iterations scales like
$\beta/t$. This convergence rate does not improve even if the function is also
strongly convex.
In this work we present a modification of the CG method tailored for convex
optimization over the spectrahedron. The per-iteration complexity of the method
is essentially identical to that of the standard CG method: only a single
eigenvecor computation is required. For minimizing an $\alpha$-strongly convex
and $\beta$-smooth function, the expected approximation error of the method
after $t$ iterations is: $$O\left({\min\{\frac{\beta{}}{t}
,\left({\frac{\beta\sqrt{\textrm{rank}(\textbf{X}^*)}}{\alpha^{1/4}t}}\right)^{4/3},
\left({\frac{\beta}{\sqrt{\alpha}\lambda_{\min}(\textbf{X}^*)t}}\right)^{2}\}}\right)
,$$ where $\textbf{X}^*$ is the optimal solution. To the best of our knowledge,
this is the first result that attains provably faster convergence rates for a
CG variant for optimization over the spectrahedron. We also present encouraging
preliminary empirical results.
| Dan Garber | null | 1605.06203 | null | null |
Convergence of Contrastive Divergence with Annealed Learning Rate in
Exponential Family | stat.ML cs.LG | In our recent paper, we showed that in exponential family, contrastive
divergence (CD) with fixed learning rate will give asymptotically consistent
estimates \cite{wu2016convergence}. In this paper, we establish consistency and
convergence rate of CD with annealed learning rate $\eta_t$. Specifically,
suppose CD-$m$ generates the sequence of parameters $\{\theta_t\}_{t \ge 0}$
using an i.i.d. data sample $\mathbf{X}_1^n \sim p_{\theta^*}$ of size $n$,
then $\delta_n(\mathbf{X}_1^n) = \limsup_{t \to \infty} \Vert \sum_{s=t_0}^t
\eta_s \theta_s / \sum_{s=t_0}^t \eta_s - \theta^* \Vert$ converges in
probability to 0 at a rate of $1/\sqrt[3]{n}$. The number ($m$) of MCMC
transitions in CD only affects the coefficient factor of convergence rate. Our
proof is not a simple extension of the one in \cite{wu2016convergence}. which
depends critically on the fact that $\{\theta_t\}_{t \ge 0}$ is a homogeneous
Markov chain conditional on the observed sample $\mathbf{X}_1^n$. Under
annealed learning rate, the homogeneous Markov property is not available and we
have to develop an alternative approach based on super-martingales. Experiment
results of CD on a fully-visible $2\times 2$ Boltzmann Machine are provided to
demonstrate our theoretical results.
| Bai Jiang, Tung-yu Wu, Wing H. Wong | null | 1605.06220 | null | null |
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks | stat.ML cs.CV cs.LG | In this paper, we introduce a new image representation based on a multilayer
kernel machine. Unlike traditional kernel methods where data representation is
decoupled from the prediction task, we learn how to shape the kernel with
supervision. We proceed by first proposing improvements of the
recently-introduced convolutional kernel networks (CKNs) in the context of
unsupervised learning; then, we derive backpropagation rules to take advantage
of labeled training data. The resulting model is a new type of convolutional
neural network, where optimizing the filters at each layer is equivalent to
learning a linear subspace in a reproducing kernel Hilbert space (RKHS). We
show that our method achieves reasonably competitive performance for image
classification on some standard "deep learning" datasets such as CIFAR-10 and
SVHN, and also for image super-resolution, demonstrating the applicability of
our approach to a large variety of image-related tasks.
| Julien Mairal | null | 1605.06265 | null | null |
Piece-wise quadratic approximations of arbitrary error functions for
fast and robust machine learning | cs.LG stat.ML | Most of machine learning approaches have stemmed from the application of
minimizing the mean squared distance principle, based on the computationally
efficient quadratic optimization methods. However, when faced with
high-dimensional and noisy data, the quadratic error functionals demonstrated
many weaknesses including high sensitivity to contaminating factors and
dimensionality curse. Therefore, a lot of recent applications in machine
learning exploited properties of non-quadratic error functionals based on $L_1$
norm or even sub-linear potentials corresponding to quasinorms $L_p$ ($0<p<1$).
The back side of these approaches is increase in computational cost for
optimization. Till so far, no approaches have been suggested to deal with {\it
arbitrary} error functionals, in a flexible and computationally efficient
framework. In this paper, we develop a theory and basic universal data
approximation algorithms ($k$-means, principal components, principal manifolds
and graphs, regularized and sparse regression), based on piece-wise quadratic
error potentials of subquadratic growth (PQSQ potentials). We develop a new and
universal framework to minimize {\it arbitrary sub-quadratic error potentials}
using an algorithm with guaranteed fast convergence to the local or global
error minimum. The theory of PQSQ potentials is based on the notion of the cone
of minorant functions, and represents a natural approximation formalism based
on the application of min-plus algebra. The approach can be applied in most of
existing machine learning methods, including methods of data approximation and
regularized and sparse regression, leading to the improvement in the
computational cost/accuracy trade-off. We demonstrate that on synthetic and
real-life datasets PQSQ-based machine learning methods achieve orders of
magnitude faster computational performance than the corresponding
state-of-the-art methods.
| A.N. Gorban, E.M. Mirkes, A. Zinovyev | 10.1016/j.neunet.2016.08.007 | 1605.06276 | null | null |
On the Robustness of Decision Tree Learning under Label Noise | cs.LG | In most practical problems of classifier learning, the training data suffers
from the label noise. Hence, it is important to understand how robust is a
learning algorithm to such label noise. This paper presents some theoretical
analysis to show that many popular decision tree algorithms are robust to
symmetric label noise under large sample size. We also present some sample
complexity results which provide some bounds on the sample size for the
robustness to hold with a high probability. Through extensive simulations we
illustrate this robustness.
| Aritra Ghosh, Naresh Manwani, P. S. Sastry | null | 1605.06296 | null | null |
Unsupervised Feature Extraction by Time-Contrastive Learning and
Nonlinear ICA | stat.ML cs.LG | Nonlinear independent component analysis (ICA) provides an appealing
framework for unsupervised feature learning, but the models proposed so far are
not identifiable. Here, we first propose a new intuitive principle of
unsupervised deep learning from time series which uses the nonstationary
structure of the data. Our learning principle, time-contrastive learning (TCL),
finds a representation which allows optimal discrimination of time segments
(windows). Surprisingly, we show how TCL can be related to a nonlinear ICA
model, when ICA is redefined to include temporal nonstationarities. In
particular, we show that TCL combined with linear ICA estimates the nonlinear
ICA model up to point-wise transformations of the sources, and this solution is
unique --- thus providing the first identifiability result for nonlinear ICA
which is rigorous, constructive, as well as very general.
| Aapo Hyvarinen and Hiroshi Morioka | null | 1605.06336 | null | null |
Fast $\epsilon$-free Inference of Simulation Models with Bayesian
Conditional Density Estimation | stat.ML cs.LG stat.CO | Many statistical models can be simulated forwards but have intractable
likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer
properties of these models from data. Traditionally these methods approximate
the posterior over parameters by conditioning on data being inside an
$\epsilon$-ball around the observed data, which is only correct in the limit
$\epsilon\!\rightarrow\!0$. Monte Carlo methods can then draw samples from the
approximate posterior to approximate predictions or error bars on parameters.
These algorithms critically slow down as $\epsilon\!\rightarrow\!0$, and in
practice draw samples from a broader distribution than the posterior. We
propose a new approach to likelihood-free inference based on Bayesian
conditional density estimation. Preliminary inferences based on limited
simulation data are used to guide later simulations. In some cases, learning an
accurate parametric representation of the entire true posterior distribution
requires fewer model simulations than Monte Carlo ABC methods need to produce a
single sample from an approximate posterior.
| George Papamakarios, Iain Murray | null | 1605.06376 | null | null |
Towards Automation of Knowledge Understanding: An Approach for
Probabilistic Generative Classifiers | cs.LG cs.AI | After data selection, pre-processing, transformation, and feature extraction,
knowledge extraction is not the final step in a data mining process. It is then
necessary to understand this knowledge in order to apply it efficiently and
effectively. Up to now, there is a lack of appropriate techniques that support
this significant step. This is partly due to the fact that the assessment of
knowledge is often highly subjective, e.g., regarding aspects such as novelty
or usefulness. These aspects depend on the specific knowledge and requirements
of the data miner. There are, however, a number of aspects that are objective
and for which it is possible to provide appropriate measures. In this article
we focus on classification problems and use probabilistic generative
classifiers based on mixture density models that are quite common in data
mining applications. We define objective measures to assess the
informativeness, uniqueness, importance, discrimination, representativity,
uncertainty, and distinguishability of rules contained in these classifiers
numerically. These measures not only support a data miner in evaluating results
of a data mining process based on such classifiers. As we will see in
illustrative case studies, they may also be used to improve the data mining
process itself or to support the later application of the extracted knowledge.
| Dominik Fisch, Christian Gruhl, Edgar Kalkowski, Bernhard Sick, Seppo
J. Ovaska | null | 1605.06377 | null | null |
Deep Multi-task Representation Learning: A Tensor Factorisation Approach | cs.LG | Most contemporary multi-task learning methods assume linear models. This
setting is considered shallow in the era of deep learning. In this paper, we
present a new deep multi-task representation learning framework that learns
cross-task sharing structure at every layer in a deep network. Our approach is
based on generalising the matrix factorisation techniques explicitly or
implicitly used by many conventional MTL algorithms to tensor factorisation, to
realise automatic learning of end-to-end knowledge sharing in deep networks.
This is in contrast to existing deep learning approaches that need a
user-defined multi-task sharing strategy. Our approach applies to both
homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our
deep multi-task representation learning in terms of both higher accuracy and
fewer design choices.
| Yongxin Yang and Timothy Hospedales | null | 1605.06391 | null | null |
Bayesian Hyperparameter Optimization for Ensemble Learning | cs.LG | In this paper, we bridge the gap between hyperparameter optimization and
ensemble learning by performing Bayesian optimization of an ensemble with
regards to its hyperparameters. Our method consists in building a fixed-size
ensemble, optimizing the configuration of one classifier of the ensemble at
each iteration of the hyperparameter optimization algorithm, taking into
consideration the interaction with the other models when evaluating potential
performances. We also consider the case where the ensemble is to be
reconstructed at the end of the hyperparameter optimization phase, through a
greedy selection over the pool of models generated during the optimization. We
study the performance of our proposed method on three different hyperparameter
spaces, showing that our approach is better than both the best single model and
a greedy ensemble construction over the models produced by a standard Bayesian
optimization.
| Julien-Charles L\'evesque, Christian Gagn\'e and Robert Sabourin | null | 1605.06394 | null | null |
Stochastic Variance Reduction Methods for Saddle-Point Problems | cs.LG math.OC | We consider convex-concave saddle-point problems where the objective
functions may be split in many components, and extend recent stochastic
variance reduction methods (such as SVRG or SAGA) to provide the first
large-scale linearly convergent algorithms for this class of problems which is
common in machine learning. While the algorithmic extension is straightforward,
it comes with challenges and opportunities: (a) the convex minimization
analysis does not apply and we use the notion of monotone operators to prove
convergence, showing in particular that the same algorithm applies to a larger
class of problems, such as variational inequalities, (b) there are two notions
of splits, in terms of functions, or in terms of partial derivatives, (c) the
split does need to be done with convex-concave terms, (d) non-uniform sampling
is key to an efficient algorithm, both in theory and practice, and (e) these
incremental algorithms can be easily accelerated using a simple extension of
the "catalyst" framework, leading to an algorithm which is always superior to
accelerated batch algorithms.
| P Balamurugan (SIERRA, LIENS), Francis Bach (SIERRA, LIENS) | null | 1605.06398 | null | null |
Ristretto: Hardware-Oriented Approximation of Convolutional Neural
Networks | cs.CV cs.LG cs.NE | Convolutional neural networks (CNN) have achieved major breakthroughs in
recent years. Their performance in computer vision have matched and in some
areas even surpassed human capabilities. Deep neural networks can capture
complex non-linear features; however this ability comes at the cost of high
computational and memory requirements. State-of-art networks require billions
of arithmetic operations and millions of parameters. To enable embedded devices
such as smartphones, Google glasses and monitoring cameras with the astonishing
power of deep learning, dedicated hardware accelerators can be used to decrease
both execution time and power consumption. In applications where fast
connection to the cloud is not guaranteed or where privacy is important,
computation needs to be done locally. Many hardware accelerators for deep
neural networks have been proposed recently. A first important step of
accelerator design is hardware-oriented approximation of deep networks, which
enables energy-efficient inference. We present Ristretto, a fast and automated
framework for CNN approximation. Ristretto simulates the hardware arithmetic of
a custom hardware accelerator. The framework reduces the bit-width of network
parameters and outputs of resource-intense layers, which reduces the chip area
for multiplication units significantly. Alternatively, Ristretto can remove the
need for multipliers altogether, resulting in an adder-only arithmetic. The
tool fine-tunes trimmed networks to achieve high classification accuracy. Since
training of deep neural networks can be time-consuming, Ristretto uses highly
optimized routines which run on the GPU. This enables fast compression of any
given network. Given a maximum tolerance of 1%, Ristretto can successfully
condense CaffeNet and SqueezeNet to 8-bit. The code for Ristretto is available.
| Philipp Gysel | null | 1605.06402 | null | null |
Data-driven root-cause analysis for distributed system anomalies | cs.LG | Modern distributed cyber-physical systems encounter a large variety of
anomalies and in many cases, they are vulnerable to catastrophic fault
propagation scenarios due to strong connectivity among the sub-systems. In this
regard, root-cause analysis becomes highly intractable due to complex fault
propagation mechanisms in combination with diverse operating modes. This paper
presents a new data-driven framework for root-cause analysis for addressing
such issues. The framework is based on a spatiotemporal feature extraction
scheme for distributed cyber-physical systems built on the concept of symbolic
dynamics for discovering and representing causal interactions among subsystems
of a complex system. We present two approaches for root-cause analysis, namely
the sequential state switching ($S^3$, based on free energy concept of a
Restricted Boltzmann Machine, RBM) and artificial anomaly association ($A^3$, a
multi-class classification framework using deep neural networks, DNN).
Synthetic data from cases with failed pattern(s) and anomalous node are
simulated to validate the proposed approaches, then compared with the
performance of vector autoregressive (VAR) model-based root-cause analysis.
Real dataset based on Tennessee Eastman process (TEP) is also used for
validation. The results show that: (1) $S^3$ and $A^3$ approaches can obtain
high accuracy in root-cause analysis and successfully handle multiple nominal
operation modes, and (2) the proposed tool-chain is shown to be scalable while
maintaining high accuracy.
| Chao Liu, Kin Gwn Lore, Soumik Sarkar | null | 1605.06421 | null | null |
Fast Randomized Semi-Supervised Clustering | cs.LG math.PR math.ST stat.ML stat.TH | We consider the problem of clustering partially labeled data from a minimal
number of randomly chosen pairwise comparisons between the items. We introduce
an efficient local algorithm based on a power iteration of the non-backtracking
operator and study its performance on a simple model. For the case of two
clusters, we give bounds on the classification error and show that a small
error can be achieved from $O(n)$ randomly chosen measurements, where $n$ is
the number of items in the dataset. Our algorithm is therefore efficient both
in terms of time and space complexities. We also investigate numerically the
performance of the algorithm on synthetic and real world data.
| Alaa Saade, Florent Krzakala, Marc Lelarge and Lenka Zdeborov\'a | 10.1088/1742-6596/1036/1/012015 | 1605.06422 | null | null |
Residual Networks Behave Like Ensembles of Relatively Shallow Networks | cs.CV cs.AI cs.LG cs.NE | In this work we propose a novel interpretation of residual networks showing
that they can be seen as a collection of many paths of differing length.
Moreover, residual networks seem to enable very deep networks by leveraging
only the short paths during training. To support this observation, we rewrite
residual networks as an explicit collection of paths. Unlike traditional
models, paths through residual networks vary in length. Further, a lesion study
reveals that these paths show ensemble-like behavior in the sense that they do
not strongly depend on each other. Finally, and most surprising, most paths are
shorter than one might expect, and only the short paths are needed during
training, as longer paths do not contribute any gradient. For example, most of
the gradient in a residual network with 110 layers comes from paths that are
only 10-34 layers deep. Our results reveal one of the key characteristics that
seem to enable the training of very deep networks: Residual networks avoid the
vanishing gradient problem by introducing short paths which can carry gradient
throughout the extent of very deep networks.
| Andreas Veit, Michael Wilber, Serge Belongie | null | 1605.06431 | null | null |
Deep Variational Bayes Filters: Unsupervised Learning of State Space
Models from Raw Data | stat.ML cs.LG cs.SY | We introduce Deep Variational Bayes Filters (DVBF), a new method for
unsupervised learning and identification of latent Markovian state space
models. Leveraging recent advances in Stochastic Gradient Variational Bayes,
DVBF can overcome intractable inference distributions via variational
inference. Thus, it can handle highly nonlinear input data with temporal and
spatial dependencies such as image sequences without domain knowledge. Our
experiments show that enabling backpropagation through transitions enforces
state space assumptions and significantly improves information content of the
latent embedding. This also enables realistic long-term prediction.
| Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der
Smagt | null | 1605.06432 | null | null |
Combining Adversarial Guarantees and Stochastic Fast Rates in Online
Learning | cs.LG | We consider online learning algorithms that guarantee worst-case regret rates
in adversarial environments (so they can be deployed safely and will perform
robustly), yet adapt optimally to favorable stochastic environments (so they
will perform well in a variety of settings of practical importance). We
quantify the friendliness of stochastic environments by means of the well-known
Bernstein (a.k.a. generalized Tsybakov margin) condition. For two recent
algorithms (Squint for the Hedge setting and MetaGrad for online convex
optimization) we show that the particular form of their data-dependent
individual-sequence regret guarantees implies that they adapt automatically to
the Bernstein parameters of the stochastic environment. We prove that these
algorithms attain fast rates in their respective settings both in expectation
and with high probability.
| Wouter M. Koolen and Peter Gr\"unwald and Tim van Erven | null | 1605.06439 | null | null |
Structured Prediction Theory Based on Factor Graph Complexity | stat.ML cs.LG | We present a general theoretical analysis of structured prediction with a
series of new results. We give new data-dependent margin guarantees for
structured prediction for a very wide family of loss functions and a general
family of hypotheses, with an arbitrary factor graph decomposition. These are
the tightest margin bounds known for both standard multi-class and general
structured prediction problems. Our guarantees are expressed in terms of a
data-dependent complexity measure, factor graph complexity, which we show can
be estimated from data and bounded in terms of familiar quantities. We further
extend our theory by leveraging the principle of Voted Risk Minimization (VRM)
and show that learning is possible even with complex factor graphs. We present
new learning bounds for this advanced setting, which we use to design two new
algorithms, Voted Conditional Random Field (VCRF) and Voted Structured Boosting
(StructBoost). These algorithms can make use of complex features and factor
graphs and yet benefit from favorable learning guarantees. We also report the
results of experiments with VCRF on several datasets to validate our theory.
| Corinna Cortes, Mehryar Mohri, Vitaly Kuznetsov, Scott Yang | null | 1605.06443 | null | null |
Unreasonable Effectiveness of Learning Neural Networks: From Accessible
States and Robust Ensembles to Basic Algorithmic Schemes | stat.ML cond-mat.dis-nn cs.LG | In artificial neural networks, learning from data is a computationally
demanding task in which a large number of connection weights are iteratively
tuned through stochastic-gradient-based heuristic processes over a
cost-function. It is not well understood how learning occurs in these systems,
in particular how they avoid getting trapped in configurations with poor
computational performance. Here we study the difficult case of networks with
discrete weights, where the optimization landscape is very rough even for
simple architectures, and provide theoretical and numerical evidence of the
existence of rare - but extremely dense and accessible - regions of
configurations in the network weight space. We define a novel measure, which we
call the "robust ensemble" (RE), which suppresses trapping by isolated
configurations and amplifies the role of these dense regions. We analytically
compute the RE in some exactly solvable models, and also provide a general
algorithmic scheme which is straightforward to implement: define a
cost-function given by a sum of a finite number of replicas of the original
cost-function, with a constraint centering the replicas around a driving
assignment. To illustrate this, we derive several powerful new algorithms,
ranging from Markov Chains to message passing to gradient descent processes,
where the algorithms target the robust dense states, resulting in substantial
improvements in performance. The weak dependence on the number of precision
bits of the weights leads us to conjecture that very similar reasoning applies
to more conventional neural networks. Analogous algorithmic schemes can also be
applied to other optimization problems.
| Carlo Baldassi, Christian Borgs, Jennifer Chayes, Alessandro Ingrosso,
Carlo Lucibello, Luca Saglietti and Riccardo Zecchina | 10.1073/pnas.1608103113 | 1605.06444 | null | null |
Query-Efficient Imitation Learning for End-to-End Autonomous Driving | cs.LG cs.AI cs.RO | One way to approach end-to-end autonomous driving is to learn a policy
function that maps from a sensory input, such as an image frame from a
front-facing camera, to a driving action, by imitating an expert driver, or a
reference policy. This can be done by supervised learning, where a policy
function is tuned to minimize the difference between the predicted and
ground-truth actions. A policy function trained in this way however is known to
suffer from unexpected behaviours due to the mismatch between the states
reachable by the reference policy and trained policy functions. More advanced
algorithms for imitation learning, such as DAgger, addresses this issue by
iteratively collecting training examples from both reference and trained
policies. These algorithms often requires a large number of queries to a
reference policy, which is undesirable as the reference policy is often
expensive. In this paper, we propose an extension of the DAgger, called
SafeDAgger, that is query-efficient and more suitable for end-to-end autonomous
driving. We evaluate the proposed SafeDAgger in a car racing simulator and show
that it indeed requires less queries to a reference policy. We observe a
significant speed up in convergence, which we conjecture to be due to the
effect of automated curriculum learning.
| Jiakai Zhang, Kyunghyun Cho | null | 1605.06450 | null | null |
Virtual Worlds as Proxy for Multi-Object Tracking Analysis | cs.CV cs.LG cs.NE stat.ML | Modern computer vision algorithms typically require expensive data
acquisition and accurate manual labeling. In this work, we instead leverage the
recent progress in computer graphics to generate fully labeled, dynamic, and
photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual
world cloning method, and validate our approach by building and publicly
releasing a new video dataset, called Virtual KITTI (see
http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds),
automatically labeled with accurate ground truth for object detection,
tracking, scene and instance segmentation, depth, and optical flow. We provide
quantitative experimental evidence suggesting that (i) modern deep learning
algorithms pre-trained on real data behave similarly in real and virtual
worlds, and (ii) pre-training on virtual data improves performance. As the gap
between real and virtual worlds is small, virtual worlds enable measuring the
impact of various weather and imaging conditions on recognition performance,
all other things being equal. We show these factors may affect drastically
otherwise high-performing deep models for tracking.
| Adrien Gaidon, Qiao Wang, Yohann Cabon, Eleonora Vig | null | 1605.06457 | null | null |
Swapout: Learning an ensemble of deep architectures | cs.CV cs.LG cs.NE | We describe Swapout, a new stochastic training method, that outperforms
ResNets of identical network structure yielding impressive results on CIFAR-10
and CIFAR-100. Swapout samples from a rich set of architectures including
dropout, stochastic depth and residual architectures as special cases. When
viewed as a regularization method swapout not only inhibits co-adaptation of
units in a layer, similar to dropout, but also across network layers. We
conjecture that swapout achieves strong regularization by implicitly tying the
parameters across layers. When viewed as an ensemble training method, it
samples a much richer set of architectures than existing methods such as
dropout or stochastic depth. We propose a parameterization that reveals
connections to exiting architectures and suggests a much richer set of
architectures to be explored. We show that our formulation suggests an
efficient training method and validate our conclusions on CIFAR-10 and
CIFAR-100 matching state of the art accuracy. Remarkably, our 32 layer wider
model performs similar to a 1001 layer ResNet model.
| Saurabh Singh, Derek Hoiem and David Forsyth | null | 1605.06465 | null | null |
Regression with n$\to$1 by Expert Knowledge Elicitation | cs.LG | We consider regression under the "extremely small $n$ large $p$" condition,
where the number of samples $n$ is so small compared to the dimensionality $p$
that predictors cannot be estimated without prior knowledge. This setup occurs
in personalized medicine, for instance, when predicting treatment outcomes for
an individual patient based on noisy high-dimensional genomics data. A
remaining source of information is expert knowledge, which has received
relatively little attention in recent years. We formulate the inference problem
of asking expert feedback on features on a budget, propose an elicitation
strategy for a simple "small $n$" setting, and derive conditions under which
the elicitation strategy is optimal. Experiments on simulated experts, both on
synthetic and genomics data, demonstrate that the proposed strategy can
drastically improve prediction accuracy.
| Marta Soare, Muhammad Ammad-ud-din, Samuel Kaski | 10.1109/ICMLA.2016.0131 | 1605.06477 | null | null |
Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups | cs.NE cs.CV cs.LG | We propose a new method for creating computationally efficient and compact
convolutional neural networks (CNNs) using a novel sparse connection structure
that resembles a tree root. This allows a significant reduction in
computational cost and number of parameters compared to state-of-the-art deep
CNNs, without compromising accuracy, by exploiting the sparsity of inter-layer
filter dependencies. We validate our approach by using it to train more
efficient variants of state-of-the-art CNN architectures, evaluated on the
CIFAR10 and ILSVRC datasets. Our results show similar or higher accuracy than
the baseline architectures with much less computation, as measured by CPU and
GPU timings. For example, for ResNet 50, our model has 40% fewer parameters,
45% fewer floating point operations, and is 31% (12%) faster on a CPU (GPU).
For the deeper ResNet 200 our model has 25% fewer floating point operations and
44% fewer parameters, while maintaining state-of-the-art accuracy. For
GoogLeNet, our model has 7% fewer parameters and is 21% (16%) faster on a CPU
(GPU).
| Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi | 10.1109/CVPR.2017.633 | 1605.06489 | null | null |
Linear-memory and Decomposition-invariant Linearly Convergent
Conditional Gradient Algorithm for Structured Polytopes | math.OC cs.LG | Recently, several works have shown that natural modifications of the
classical conditional gradient method (aka Frank-Wolfe algorithm) for
constrained convex optimization, provably converge with a linear rate when: i)
the feasible set is a polytope, and ii) the objective is smooth and
strongly-convex. However, all of these results suffer from two significant
shortcomings: large memory requirement due to the need to store an explicit
convex decomposition of the current iterate, and as a consequence, large
running-time overhead per iteration, and worst case convergence rate that
depends unfavorably on the dimension.
In this work we present a new conditional gradient variant and a
corresponding analysis that improves on both of the above shortcomings. In
particular: both memory and computation overheads are only linear in the
dimension. Moreover, in case the optimal solution is sparse, the new
convergence rate replaces a factor which is at least linear in the dimension in
previous works, with a linear dependence on the number of non-zeros in the
optimal solution.
At the heart of our method, and corresponding analysis, is a novel way to
compute decomposition-invariant away-steps. While our theoretical guarantees do
not apply to any polytope, they apply to several important structured polytopes
that capture central concepts such as paths in graphs, perfect matchings in
bipartite graphs, marginal distributions that arise in structured prediction
tasks, and more. Our theoretical findings are complemented by empirical
evidence which shows that our method delivers state-of-the-art performance.
| Dan Garber and Ofer Meshi | null | 1605.06492 | null | null |
TensorLog: A Differentiable Deductive Database | cs.AI cs.DB cs.LG | Large knowledge bases (KBs) are useful in many tasks, but it is unclear how
to integrate this sort of knowledge into "deep" gradient-based learning
systems. To address this problem, we describe a probabilistic deductive
database, called TensorLog, in which reasoning uses a differentiable process.
In TensorLog, each clause in a logical theory is first converted into certain
type of factor graph. Then, for each type of query to the factor graph, the
message-passing steps required to perform belief propagation (BP) are
"unrolled" into a function, which is differentiable. We show that these
functions can be composed recursively to perform inference in non-trivial
logical theories containing multiple interrelated clauses and predicates. Both
compilation and inference in TensorLog are efficient: compilation is linear in
theory size and proof depth, and inference is linear in database size and the
number of message-passing steps used in BP. We also present experimental
results with TensorLog and discuss its relationship to other first-order
probabilistic logics.
| William W. Cohen | null | 1605.06523 | null | null |
Functional Hashing for Compressing Neural Networks | cs.LG cs.NE | As the complexity of deep neural networks (DNNs) trend to grow to absorb the
increasing sizes of data, memory and energy consumption has been receiving more
and more attentions for industrial applications, especially on mobile devices.
This paper presents a novel structure based on functional hashing to compress
DNNs, namely FunHashNN. For each entry in a deep net, FunHashNN uses multiple
low-cost hash functions to fetch values in the compression space, and then
employs a small reconstruction network to recover that entry. The
reconstruction network is plugged into the whole network and trained jointly.
FunHashNN includes the recently proposed HashedNets as a degenerated case, and
benefits from larger value capacity and less reconstruction loss. We further
discuss extensions with dual space hashing and multi-hops. On several benchmark
datasets, FunHashNN demonstrates high compression ratios with little loss on
prediction accuracy.
| Lei Shi and Shikun Feng and Zhifan Zhu | null | 1605.06560 | null | null |
DynaNewton - Accelerating Newton's Method for Machine Learning | cs.LG | Newton's method is a fundamental technique in optimization with quadratic
convergence within a neighborhood around the optimum. However reaching this
neighborhood is often slow and dominates the computational costs. We exploit
two properties specific to empirical risk minimization problems to accelerate
Newton's method, namely, subsampling training data and increasing strong
convexity through regularization. We propose a novel continuation method, where
we define a family of objectives over increasing sample sizes and with
decreasing regularization strength. Solutions on this path are tracked such
that the minimizer of the previous objective is guaranteed to be within the
quadratic convergence region of the next objective to be optimized. Thereby
every Newton iteration is guaranteed to achieve super-linear contractions with
regard to the chosen objective, which becomes a moving target. We provide a
theoretical analysis that motivates our algorithm, called DynaNewton, and
characterizes its speed of convergence. Experiments on a wide range of data
sets and problems consistently confirm the predicted computational savings.
| Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann | null | 1605.06561 | null | null |
Online Influence Maximization under Independent Cascade Model with
Semi-Bandit Feedback | cs.LG cs.AI cs.SI math.OC stat.ML | We study the online influence maximization problem in social networks under
the independent cascade model. Specifically, we aim to learn the set of "best
influencers" in a social network online while repeatedly interacting with it.
We address the challenges of (i) combinatorial action space, since the number
of feasible influencer sets grows exponentially with the maximum number of
influencers, and (ii) limited feedback, since only the influenced portion of
the network is observed. Under a stochastic semi-bandit feedback, we propose
and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our
bounds on the cumulative regret are polynomial in all quantities of interest,
achieve near-optimal dependence on the number of interactions and reflect the
topology of the network and the activation probabilities of its edges, thereby
giving insights on the problem complexity. To the best of our knowledge, these
are the first such results. Our experiments show that in several representative
graph topologies, the regret of IMLinUCB scales as suggested by our upper
bounds. IMLinUCB permits linear generalization and thus is both statistically
and computationally suitable for large-scale problems. Our experiments also
show that IMLinUCB with linear generalization can lead to low regret in
real-world online influence maximization.
| Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani | null | 1605.06593 | null | null |
Make Workers Work Harder: Decoupled Asynchronous Proximal Stochastic
Gradient Descent | math.OC cs.DC cs.LG stat.ML | Asynchronous parallel optimization algorithms for solving large-scale machine
learning problems have drawn significant attention from academia to industry
recently. This paper proposes a novel algorithm, decoupled asynchronous
proximal stochastic gradient descent (DAP-SGD), to minimize an objective
function that is the composite of the average of multiple empirical losses and
a regularization term. Unlike the traditional asynchronous proximal stochastic
gradient descent (TAP-SGD) in which the master carries much of the computation
load, the proposed algorithm off-loads the majority of computation tasks from
the master to workers, and leaves the master to conduct simple addition
operations. This strategy yields an easy-to-parallelize algorithm, whose
performance is justified by theoretical convergence analyses. To be specific,
DAP-SGD achieves an $O(\log T/T)$ rate when the step-size is diminishing and an
ergodic $O(1/\sqrt{T})$ rate when the step-size is constant, where $T$ is the
number of total iterations.
| Yitan Li, Linli Xu, Xiaowei Zhong, Qing Ling | null | 1605.06619 | null | null |
Deep Transfer Learning with Joint Adaptation Networks | cs.LG stat.ML | Deep networks have been successfully applied to learn transferable features
for adapting models from a source domain to a different target domain. In this
paper, we present joint adaptation networks (JAN), which learn a transfer
network by aligning the joint distributions of multiple domain-specific layers
across domains based on a joint maximum mean discrepancy (JMMD) criterion.
Adversarial training strategy is adopted to maximize JMMD such that the
distributions of the source and target domains are made more distinguishable.
Learning can be performed by stochastic gradient descent with the gradients
computed by back-propagation in linear-time. Experiments testify that our model
yields state of the art results on standard datasets.
| Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan | null | 1605.06636 | null | null |
Programming with a Differentiable Forth Interpreter | cs.NE cs.AI cs.LG | Given that in practice training data is scarce for all but a small set of
problems, a core question is how to incorporate prior knowledge into a model.
In this paper, we consider the case of prior procedural knowledge for neural
networks, such as knowing how a program should traverse a sequence, but not
what local actions should be performed at each step. To this end, we present an
end-to-end differentiable interpreter for the programming language Forth which
enables programmers to write program sketches with slots that can be filled
with behaviour trained from program input-output data. We can optimise this
behaviour directly through gradient descent techniques on user-specified
objectives, and also integrate the program into any larger neural computation
graph. We show empirically that our interpreter is able to effectively leverage
different levels of prior program structure and learn complex behaviours such
as sequence sorting and addition. When connected to outputs of an LSTM and
trained jointly, our interpreter achieves state-of-the-art accuracy for
end-to-end reasoning about quantities expressed in natural language stories.
| Matko Bo\v{s}njak, Tim Rockt\"aschel, Jason Naradowsky, Sebastian
Riedel | null | 1605.06640 | null | null |
Latent Tree Models for Hierarchical Topic Detection | cs.CL cs.IR cs.LG stat.ML | We present a novel method for hierarchical topic detection where topics are
obtained by clustering documents in multiple ways. Specifically, we model
document collections using a class of graphical models called hierarchical
latent tree models (HLTMs). The variables at the bottom level of an HLTM are
observed binary variables that represent the presence/absence of words in a
document. The variables at other levels are binary latent variables, with those
at the lowest latent level representing word co-occurrence patterns and those
at higher levels representing co-occurrence of patterns at the level below.
Each latent variable gives a soft partition of the documents, and document
clusters in the partitions are interpreted as topics. Latent variables at high
levels of the hierarchy capture long-range word co-occurrence patterns and
hence give thematically more general topics, while those at low levels of the
hierarchy capture short-range word co-occurrence patterns and give thematically
more specific topics. Unlike LDA-based topic models, HLTMs do not refer to a
document generation process and use word variables instead of token variables.
They use a tree structure to model the relationships between topics and words,
which is conducive to the discovery of meaningful topics and topic hierarchies.
| Peixian Chen, Nevin L. Zhang, Tengfei Liu, Leonard K.M. Poon, Zhourong
Chen and Farhan Khawar | null | 1605.06650 | null | null |
Gambler's Ruin Bandit Problem | cs.LG | In this paper, we propose a new multi-armed bandit problem called the
Gambler's Ruin Bandit Problem (GRBP). In the GRBP, the learner proceeds in a
sequence of rounds, where each round is a Markov Decision Process (MDP) with
two actions (arms): a continuation action that moves the learner randomly over
the state space around the current state; and a terminal action that moves the
learner directly into one of the two terminal states (goal and dead-end state).
The current round ends when a terminal state is reached, and the learner incurs
a positive reward only when the goal state is reached. The objective of the
learner is to maximize its long-term reward (expected number of times the goal
state is reached), without having any prior knowledge on the state transition
probabilities. We first prove a result on the form of the optimal policy for
the GRBP. Then, we define the regret of the learner with respect to an
omnipotent oracle, which acts optimally in each round, and prove that it
increases logarithmically over rounds. We also identify a condition under which
the learner's regret is bounded. A potential application of the GRBP is optimal
medical treatment assignment, in which the continuation action corresponds to a
conservative treatment and the terminal action corresponds to a risky treatment
such as surgery.
| Nima Akbarzadeh, Cem Tekin | null | 1605.06651 | null | null |
Bending the Curve: Improving the ROC Curve Through Error Redistribution | cs.LG | Classification performance is often not uniform over the data. Some areas in
the input space are easier to classify than others. Features that hold
information about the "difficulty" of the data may be non-discriminative and
are therefore disregarded in the classification process. We propose a
meta-learning approach where performance may be improved by post-processing.
This improvement is done by establishing a dynamic threshold on the
base-classifier results. Since the base-classifier is treated as a "black box"
the method presented can be used on any state of the art classifier in order to
try an improve its performance. We focus our attention on how to better control
the true-positive/false-positive trade-off known as the ROC curve. We propose
an algorithm for the derivation of optimal thresholds by redistributing the
error depending on features that hold information about difficulty. We
demonstrate the resulting benefit on both synthetic and real-life data.
| Oran Richman, Shie Mannor | null | 1605.06652 | null | null |
Cross Domain Adaptation by Learning Partially Shared Classifiers and
Weighting Source Data Points in the Shared Subspaces | cs.LG | Transfer learning is a problem defined over two domains. These two domains
share the same feature space and class label space, but have significantly
different distributions. One domain has sufficient labels, named as source
domain, and the other domain has few labels, named as target do- main. The
problem is to learn a effective classifier for the target domain. In this
paper, we propose a novel transfer learning method for this problem by learning
a partially shared classifier for the target domain, and weighting the source
domain data points. We learn some shared subspaces for both the data points of
the two domains, and a shared classifier in the shared subspaces. We hope that
in the shared subspaces, the distributions of two domain can match each other
well, and to match the distributions, we weight the source domain data points
with different weighting factors. Moreover, we adapt the shared classifier to
each domain by learning different adaptation functions. To learn the subspace
transformation matrices, the classifier parameters, and the adaptation
parameters, we build a objective function with weighted clas- sification
errors, parameter regularization, local reconstruction regularization, and
distribution matching. This objective function is minimized by an itera- tive
algorithm. Experiments show its effectiveness over benchmark data sets,
including travel destination review data set, face expression data set, spam
email data set, etc.
| Hongqi Wang, Anfeng Xu, Shanshan Wang, Sunny Chughtai | null | 1605.06673 | null | null |
Learning to Communicate with Deep Multi-Agent Reinforcement Learning | cs.AI cs.LG cs.MA | We consider the problem of multiple agents sensing and acting in environments
with the goal of maximising their shared utility. In these environments, agents
must learn communication protocols in order to share information that is needed
to solve the tasks. By embracing deep neural networks, we are able to
demonstrate end-to-end learning of protocols in complex environments inspired
by communication riddles and multi-agent computer vision problems with partial
observability. We propose two approaches for learning in these domains:
Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning
(DIAL). The former uses deep Q-learning, while the latter exploits the fact
that, during learning, agents can backpropagate error derivatives through
(noisy) communication channels. Hence, this approach uses centralised learning
but decentralised execution. Our experiments introduce new environments for
studying the learning of communication protocols and present a set of
engineering innovations that are essential for success in these domains.
| Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson | null | 1605.06676 | null | null |
Efficient Document Indexing Using Pivot Tree | cs.IR cs.LG | We present a novel method for efficiently searching top-k neighbors for
documents represented in high dimensional space of terms based on the cosine
similarity. Mostly, documents are stored as bag-of-words tf-idf representation.
One of the most used ways of computing similarity between a pair of documents
is cosine similarity between the vector representations, but cosine similarity
is not a metric distance measure as it doesn't follow triangle inequality,
therefore most metric searching methods can not be applied directly. We propose
an efficient method for indexing documents using a pivot tree that leads to
efficient retrieval. We also study the relation between precision and
efficiency for the proposed method and compare it with a state of the art in
the area of document searching based on inner product.
| Gaurav Singh and Benjamin Piwowarski | null | 1605.06693 | null | null |
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering | cs.LG stat.ML | Dimensionality reduction techniques play an essential role in data analytics,
signal processing and machine learning. Dimensionality reduction is usually
performed in a preprocessing stage that is separate from subsequent data
analysis, such as clustering or classification. Finding reduced-dimension
representations that are well-suited for the intended task is more appealing.
This paper proposes a joint factor analysis and latent clustering framework,
which aims at learning cluster-aware low-dimensional representations of matrix
and tensor data. The proposed approach leverages matrix and tensor
factorization models that produce essentially unique latent representations of
the data to unravel latent cluster structure -- which is otherwise obscured
because of the freedom to apply an oblique transformation in latent space. At
the same time, latent cluster structure is used as prior information to enhance
the performance of factorization. Specific contributions include several
custom-built problem formulations, corresponding algorithms, and discussion of
associated convergence properties. Besides extensive simulations, real-world
datasets such as Reuters document data and MNIST image data are also employed
to showcase the effectiveness of the proposed approaches.
| Bo Yang, Xiao Fu and Nicholas D. Sidiropoulos | 10.1109/TSP.2016.2614491 | 1605.06711 | null | null |
Factored Temporal Sigmoid Belief Networks for Sequence Learning | stat.ML cs.LG | Deep conditional generative models are developed to simultaneously learn the
temporal dependencies of multiple sequences. The model is designed by
introducing a three-way weight tensor to capture the multiplicative
interactions between side information and sequences. The proposed model builds
on the Temporal Sigmoid Belief Network (TSBN), a sequential stack of Sigmoid
Belief Networks (SBNs). The transition matrices are further factored to reduce
the number of parameters and improve generalization. When side information is
not available, a general framework for semi-supervised learning based on the
proposed model is constituted, allowing robust sequence classification.
Experimental results show that the proposed approach achieves state-of-the-art
predictive and classification performance on sequential data, and has the
capacity to synthesize sequences, with controlled style transitioning and
blending.
| Jiaming Song, Zhe Gan, Lawrence Carin | null | 1605.06715 | null | null |
A Rapid Pattern-Recognition Method for Driving Types Using
Clustering-Based Support Vector Machines | stat.ML cs.CV cs.LG | A rapid pattern-recognition approach to characterize driver's
curve-negotiating behavior is proposed. To shorten the recognition time and
improve the recognition of driving styles, a k-means clustering-based support
vector machine ( kMC-SVM) method is developed and used for classifying drivers
into two types: aggressive and moderate. First, vehicle speed and throttle
opening are treated as the feature parameters to reflect the driving styles.
Second, to discriminate driver curve-negotiating behaviors and reduce the
number of support vectors, the k-means clustering method is used to extract and
gather the two types of driving data and shorten the recognition time. Then,
based on the clustering results, a support vector machine approach is utilized
to generate the hyperplane for judging and predicting to which types the human
driver are subject. Lastly, to verify the validity of the kMC-SVM method, a
cross-validation experiment is designed and conducted. The research results
show that the $ k $MC-SVM is an effective method to classify driving styles
with a short time, compared with SVM method.
| Wenshuo Wang and Junqiang Xi | 10.1109/ACC.2016.7526495 | 1605.06742 | null | null |
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