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A Non-Parametric Control Chart For High Frequency Multivariate Data | cs.LG stat.AP stat.ME stat.ML | Support Vector Data Description (SVDD) is a machine learning technique used
for single class classification and outlier detection. SVDD based K-chart was
first introduced by Sun and Tsung for monitoring multivariate processes when
underlying distribution of process parameters or quality characteristics depart
from Normality. The method first trains a SVDD model on data obtained from
stable or in-control operations of the process to obtain a threshold $R^2$ and
kernel center a. For each new observation, its Kernel distance from the Kernel
center a is calculated. The kernel distance is compared against the threshold
$R^2$ to determine if the observation is within the control limits. The
non-parametric K-chart provides an attractive alternative to the traditional
control charts such as the Hotelling's $T^2$ charts when distribution of the
underlying multivariate data is either non-normal or is unknown. But there are
challenges when K-chart is deployed in practice. The K-chart requires
calculating kernel distance of each new observation but there are no guidelines
on how to interpret the kernel distance plot and infer about shifts in process
mean or changes in process variation. This limits the application of K-charts
in big-data applications such as equipment health monitoring, where
observations are generated at a very high frequency. In this scenario, the
analyst using the K-chart is inundated with kernel distance results at a very
high frequency, generally without any recourse for detecting presence of any
assignable causes of variation. We propose a new SVDD based control chart,
called as $K_T$ chart, which addresses challenges encountered when using
K-chart for big-data applications. The $K_T$ charts can be used to
simultaneously track process variation and central tendency. We illustrate the
successful use of $K_T$ chart using the Tennessee Eastman process data.
| Deovrat Kakde, Sergriy Peredriy, Arin Chaudhuri, Anya Mcguirk | 10.1109/RAM.2017.7889786 | 1607.07423 | null | null |
Deepr: A Convolutional Net for Medical Records | stat.ML cs.LG | Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space.
| Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh | null | 1607.07519 | null | null |
On the Resistance of Nearest Neighbor to Random Noisy Labels | cs.LG | Nearest neighbor has always been one of the most appealing non-parametric
approaches in machine learning, pattern recognition, computer vision, etc.
Previous empirical studies partly shows that nearest neighbor is resistant to
noise, yet there is a lack of deep analysis. This work presents the
finite-sample and distribution-dependent bounds on the consistency of nearest
neighbor in the random noise setting. The theoretical results show that, for
asymmetric noises, k-nearest neighbor is robust enough to classify most data
correctly, except for a handful of examples, whose labels are totally misled by
random noises. For symmetric noises, however, k-nearest neighbor achieves the
same consistent rate as that of noise-free setting, which verifies the
resistance of k-nearest neighbor to random noisy labels. Motivated by the
theoretical analysis, we propose the Robust k-Nearest Neighbor (RkNN) approach
to deal with noisy labels. The basic idea is to make unilateral corrections to
examples, whose labels are totally misled by random noises, and classify the
others directly by utilizing the robustness of k-nearest neighbor. We verify
the effectiveness of the proposed algorithm both theoretically and empirically.
| Wei Gao and Bin-Bin Yang and Zhi-Hua Zhou | null | 1607.07526 | null | null |
Simultaneous Estimation of Noise Variance and Number of Peaks in
Bayesian Spectral Deconvolution | physics.data-an cs.LG stat.ML | The heuristic identification of peaks from noisy complex spectra often leads
to misunderstanding of the physical and chemical properties of matter. In this
paper, we propose a framework based on Bayesian inference, which enables us to
separate multipeak spectra into single peaks statistically and consists of two
steps. The first step is estimating both the noise variance and the number of
peaks as hyperparameters based on Bayes free energy, which generally is not
analytically tractable. The second step is fitting the parameters of each peak
function to the given spectrum by calculating the posterior density, which has
a problem of local minima and saddles since multipeak models are nonlinear and
hierarchical. Our framework enables the escape from local minima or saddles by
using the exchange Monte Carlo method and calculates Bayes free energy via the
multiple histogram method. We discuss a simulation demonstrating how efficient
our framework is and show that estimating both the noise variance and the
number of peaks prevents overfitting, overpenalizing, and misunderstanding the
precision of parameter estimation.
| Satoru Tokuda, Kenji Nagata, and Masato Okada | 10.7566/JPSJ.86.024001 | 1607.0759 | null | null |
Adaptive Nonnegative Matrix Factorization and Measure Comparisons for
Recommender Systems | cs.LG cs.NA math.NA stat.ML | The Nonnegative Matrix Factorization (NMF) of the rating matrix has shown to
be an effective method to tackle the recommendation problem. In this paper we
propose new methods based on the NMF of the rating matrix and we compare them
with some classical algorithms such as the SVD and the regularized and
unregularized non-negative matrix factorization approach. In particular a new
algorithm is obtained changing adaptively the function to be minimized at each
step, realizing a sort of dynamic prior strategy. Another algorithm is obtained
modifying the function to be minimized in the NMF formulation by enforcing the
reconstruction of the unknown ratings toward a prior term. We then combine
different methods obtaining two mixed strategies which turn out to be very
effective in the reconstruction of missing observations. We perform a
thoughtful comparison of different methods on the basis of several evaluation
measures. We consider in particular rating, classification and ranking measures
showing that the algorithm obtaining the best score for a given measure is in
general the best also when different measures are considered, lowering the
interest in designing specific evaluation measures. The algorithms have been
tested on different datasets, in particular the 1M, and 10M MovieLens datasets
containing ratings on movies, the Jester dataset with ranting on jokes and
Amazon Fine Foods dataset with ratings on foods. The comparison of the
different algorithms, shows the good performance of methods employing both an
explicit and an implicit regularization scheme. Moreover we can get a boost by
mixed strategies combining a fast method with a more accurate one.
| Gianna M. Del Corso and Francesco Romani | 10.1016/j.amc.2019.01.047 | 1607.07607 | null | null |
Learning Null Space Projections in Operational Space Formulation | cs.LG cs.RO | In recent years, a number of tools have become available that recover the
underlying control policy from constrained movements. However, few have
explicitly considered learning the constraints of the motion and ways to cope
with unknown environment. In this paper, we consider learning the null space
projection matrix of a kinematically constrained system in the absence of any
prior knowledge either on the underlying policy, the geometry, or
dimensionality of the constraints. Our evaluations have demonstrated the
effectiveness of the proposed approach on problems of differing dimensionality,
and with different degrees of non-linearity.
| Hsiu-Chin Lin and Matthew Howard | null | 1607.07611 | null | null |
The Price of Anarchy in Auctions | cs.GT cs.AI cs.LG | This survey outlines a general and modular theory for proving approximation
guarantees for equilibria of auctions in complex settings. This theory
complements traditional economic techniques, which generally focus on exact and
optimal solutions and are accordingly limited to relatively stylized settings.
We highlight three user-friendly analytical tools: smoothness-type
inequalities, which immediately yield approximation guarantees for many auction
formats of interest in the special case of complete information and
deterministic strategies; extension theorems, which extend such guarantees to
randomized strategies, no-regret learning outcomes, and incomplete-information
settings; and composition theorems, which extend such guarantees from simpler
to more complex auctions. Combining these tools yields tight worst-case
approximation guarantees for the equilibria of many widely-used auction
formats.
| Tim Roughgarden, Vasilis Syrgkanis, Eva Tardos | null | 1607.07684 | null | null |
Hierarchical Multi-resolution Mesh Networks for Brain Decoding | cs.NE cs.CV cs.LG | We propose a new framework, called Hierarchical Multi-resolution Mesh
Networks (HMMNs), which establishes a set of brain networks at multiple time
resolutions of fMRI signal to represent the underlying cognitive process. The
suggested framework, first, decomposes the fMRI signal into various frequency
subbands using wavelet transforms. Then, a brain network, called mesh network,
is formed at each subband by ensembling a set of local meshes. The locality
around each anatomic region is defined with respect to a neighborhood system
based on functional connectivity. The arc weights of a mesh are estimated by
ridge regression formed among the average region time series. In the final
step, the adjacency matrices of mesh networks obtained at different subbands
are ensembled for brain decoding under a hierarchical learning architecture,
called, fuzzy stacked generalization (FSG). Our results on Human Connectome
Project task-fMRI dataset reflect that the suggested HMMN model can
successfully discriminate tasks by extracting complementary information
obtained from mesh arc weights of multiple subbands. We study the topological
properties of the mesh networks at different resolutions using the network
measures, namely, node degree, node strength, betweenness centrality and global
efficiency; and investigate the connectivity of anatomic regions, during a
cognitive task. We observe significant variations among the network topologies
obtained for different subbands. We, also, analyze the diversity properties of
classifier ensemble, trained by the mesh networks in multiple subbands and
observe that the classifiers in the ensemble collaborate with each other to
fuse the complementary information freed at each subband. We conclude that the
fMRI data, recorded during a cognitive task, embed diverse information across
the anatomic regions at each resolution.
| Itir Onal Ertugrul, Mete Ozay, Fatos Tunay Yarman Vural | null | 1607.07695 | null | null |
Machine Learning in Falls Prediction; A cognition-based predictor of
falls for the acute neurological in-patient population | cs.CY cs.LG | Background Information: Falls are associated with high direct and indirect
costs, and significant morbidity and mortality for patients. Pathological falls
are usually a result of a compromised motor system, and/or cognition. Very
little research has been conducted on predicting falls based on this premise.
Aims: To demonstrate that cognitive and motor tests can be used to create a
robust predictive tool for falls.
Methods: Three tests of attention and executive function (Stroop, Trail
Making, and Semantic Fluency), a measure of physical function (Walk-12), a
series of questions (concerning recent falls, surgery and physical function)
and demographic information were collected from a cohort of 323 patients at a
tertiary neurological center. The principal outcome was a fall during the
in-patient stay (n = 54). Data-driven, predictive modelling was employed to
identify the statistical modelling strategies which are most accurate in
predicting falls, and which yield the most parsimonious models of clinical
relevance.
Results: The Trail test was identified as the best predictor of falls.
Moreover, addition of any others variables, to the results of the Trail test
did not improve the prediction (Wilcoxon signed-rank p < .001). The best
statistical strategy for predicting falls was the random forest (Wilcoxon
signed-rank p < .001), based solely on results of the Trail test. Tuning of the
model results in the following optimized values: 68% (+- 7.7) sensitivity, 90%
(+- 2.3) specificity, with a positive predictive value of 60%, when the
relevant data is available.
Conclusion: Predictive modelling has identified a simple yet powerful machine
learning prediction strategy based on a single clinical test, the Trail test.
Predictive evaluation shows this strategy to be robust, suggesting predictive
modelling and machine learning as the standard for future predictive tools.
| Bilal A. Mateen and Matthias Bussas and Catherine Doogan and Denise
Waller and Alessia Saverino and Franz J Kir\'aly and E Diane Playford | 10.1177/0269215518771127 | 1607.07751 | null | null |
Focused Model-Learning and Planning for Non-Gaussian Continuous
State-Action Systems | cs.AI cs.LG cs.RO stat.AP stat.ML | We introduce a framework for model learning and planning in stochastic
domains with continuous state and action spaces and non-Gaussian transition
models. It is efficient because (1) local models are estimated only when the
planner requires them; (2) the planner focuses on the most relevant states to
the current planning problem; and (3) the planner focuses on the most
informative and/or high-value actions. Our theoretical analysis shows the
validity and asymptotic optimality of the proposed approach. Empirically, we
demonstrate the effectiveness of our algorithm on a simulated multi-modal
pushing problem.
| Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Tom\'as
Lozano-P\'erez | null | 1607.07762 | null | null |
Error-Resilient Machine Learning in Near Threshold Voltage via
Classifier Ensemble | cs.LG | In this paper, we present the design of error-resilient machine learning
architectures by employing a distributed machine learning framework referred to
as classifier ensemble (CE). CE combines several simple classifiers to obtain a
strong one. In contrast, centralized machine learning employs a single complex
block. We compare the random forest (RF) and the support vector machine (SVM),
which are representative techniques from the CE and centralized frameworks,
respectively. Employing the dataset from UCI machine learning repository and
architectural-level error models in a commercial 45 nm CMOS process, it is
demonstrated that RF-based architectures are significantly more robust than SVM
architectures in presence of timing errors due to process variations in
near-threshold voltage (NTV) regions (0.3 V - 0.7 V). In particular, the RF
architecture exhibits a detection accuracy (P_{det}) that varies by 3.2% while
maintaining a median P_{det} > 0.9 at a gate level delay variation of 28.9% .
In comparison, SVM exhibits a P_{det} that varies by 16.8%. Additionally, we
propose an error weighted voting technique that incorporates the timing error
statistics of the NTV circuit fabric to further enhance robustness. Simulation
results confirm that the error weighted voting achieves a P_{det} that varies
by only 1.4%, which is 12X lower compared to SVM.
| Sai Zhang, Naresh Shanbhag | null | 1607.07804 | null | null |
Prediction of future hospital admissions - what is the tradeoff between
specificity and accuracy? | q-bio.QM cs.LG | Large amounts of electronic medical records collected by hospitals across the
developed world offer unprecedented possibilities for knowledge discovery using
computer based data mining and machine learning. Notwithstanding significant
research efforts, the use of this data in the prediction of disease development
has largely been disappointing. In this paper we examine in detail a recently
proposed method which has in preliminary experiments demonstrated highly
promising results on real-world data. We scrutinize the authors' claims that
the proposed model is scalable and investigate whether the tradeoff between
prediction specificity (i.e. the ability of the model to predict a wide number
of different ailments) and accuracy (i.e. the ability of the model to make the
correct prediction) is practically viable. Our experiments conducted on a data
corpus of nearly 3,000,000 admissions support the authors' expectations and
demonstrate that the high prediction accuracy is maintained well even when the
number of admission types explicitly included in the model is increased to
account for 98% of all admissions in the corpus. Thus several promising
directions for future work are highlighted.
| Ieva Vasiljeva and Ognjen Arandjelovic | null | 1607.07817 | null | null |
First Efficient Convergence for Streaming k-PCA: a Global, Gap-Free, and
Near-Optimal Rate | math.OC cs.DS cs.LG math.NA stat.ML | We study streaming principal component analysis (PCA), that is to find, in
$O(dk)$ space, the top $k$ eigenvectors of a $d\times d$ hidden matrix $\bf
\Sigma$ with online vectors drawn from covariance matrix $\bf \Sigma$.
We provide $\textit{global}$ convergence for Oja's algorithm which is
popularly used in practice but lacks theoretical understanding for $k>1$. We
also provide a modified variant $\mathsf{Oja}^{++}$ that runs $\textit{even
faster}$ than Oja's. Our results match the information theoretic lower bound in
terms of dependency on error, on eigengap, on rank $k$, and on dimension $d$,
up to poly-log factors. In addition, our convergence rate can be made gap-free,
that is proportional to the approximation error and independent of the
eigengap.
In contrast, for general rank $k$, before our work (1) it was open to design
any algorithm with efficient global convergence rate; and (2) it was open to
design any algorithm with (even local) gap-free convergence rate in $O(dk)$
space.
| Zeyuan Allen-Zhu, Yuanzhi Li | null | 1607.07837 | null | null |
Product Offerings in Malicious Hacker Markets | cs.CR cs.LG | Marketplaces specializing in malicious hacking products - including malware
and exploits - have recently become more prominent on the darkweb and deepweb.
We scrape 17 such sites and collect information about such products in a
unified database schema. Using a combination of manual labeling and
unsupervised clustering, we examine a corpus of products in order to understand
their various categories and how they become specialized with respect to vendor
and marketplace. This initial study presents how we effectively employed
unsupervised techniques to this data as well as the types of insights we gained
on various categories of malicious hacking products.
| Ericsson Marin, Ahmad Diab and Paulo Shakarian | null | 1607.07903 | null | null |
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot
Interaction | cs.RO cs.LG | Modeling of physical human-robot collaborations is generally a challenging
problem due to the unpredictive nature of human behavior. To address this
issue, we present a data-efficient reinforcement learning framework which
enables a robot to learn how to collaborate with a human partner. The robot
learns the task from its own sensorimotor experiences in an unsupervised
manner. The uncertainty of the human actions is modeled using Gaussian
processes (GP) to implement action-value functions. Optimal action selection
given the uncertain GP model is ensured by Bayesian optimization. We apply the
framework to a scenario in which a human and a PR2 robot jointly control the
ball position on a plank based on vision and force/torque data. Our
experimental results show the suitability of the proposed method in terms of
fast and data-efficient model learning, optimal action selection under
uncertainties and equal role sharing between the partners.
| Ali Ghadirzadeh, Judith B\"utepage, Atsuto Maki, Danica Kragic and
M{\aa}rten Bj\"orkman | null | 1607.07939 | null | null |
Using Kernel Methods and Model Selection for Prediction of Preterm Birth | cs.LG stat.ML | We describe an application of machine learning to the problem of predicting
preterm birth. We conduct a secondary analysis on a clinical trial dataset
collected by the National In- stitute of Child Health and Human Development
(NICHD) while focusing our attention on predicting different classes of preterm
birth. We compare three approaches for deriving predictive models: a support
vector machine (SVM) approach with linear and non-linear kernels, logistic
regression with different model selection along with a model based on decision
rules prescribed by physician experts for prediction of preterm birth. Our
approach highlights the pre-processing methods applied to handle the inherent
dynamics, noise and gaps in the data and describe techniques used to handle
skewed class distributions. Empirical experiments demonstrate significant
improvement in predicting preterm birth compared to past work.
| Ilia Vovsha, Ansaf Salleb-Aouissi, Anita Raja, Thomas Koch, Alex
Rybchuk, Axinia Radeva, Ashwath Rajan, Yiwen Huang, Hatim Diab, Ashish Tomar,
and Ronald Wapner | null | 1607.07959 | null | null |
Learning of Generalized Low-Rank Models: A Greedy Approach | cs.LG cs.NA math.OC | Learning of low-rank matrices is fundamental to many machine learning
applications. A state-of-the-art algorithm is the rank-one matrix pursuit
(R1MP). However, it can only be used in matrix completion problems with the
square loss. In this paper, we develop a more flexible greedy algorithm for
generalized low-rank models whose optimization objective can be smooth or
nonsmooth, general convex or strongly convex. The proposed algorithm has low
per-iteration time complexity and fast convergence rate. Experimental results
show that it is much faster than the state-of-the-art, with comparable or even
better prediction performance.
| Quanming Yao and James T. Kwok | null | 1607.08012 | null | null |
CNN-based Patch Matching for Optical Flow with Thresholded Hinge
Embedding Loss | cs.CV cs.LG cs.NE | Learning based approaches have not yet achieved their full potential in
optical flow estimation, where their performance still trails heuristic
approaches. In this paper, we present a CNN based patch matching approach for
optical flow estimation. An important contribution of our approach is a novel
thresholded loss for Siamese networks. We demonstrate that our loss performs
clearly better than existing losses. It also allows to speed up training by a
factor of 2 in our tests. Furthermore, we present a novel way for calculating
CNN based features for different image scales, which performs better than
existing methods. We also discuss new ways of evaluating the robustness of
trained features for the application of patch matching for optical flow. An
interesting discovery in our paper is that low-pass filtering of feature maps
can increase the robustness of features created by CNNs. We proved the
competitive performance of our approach by submitting it to the KITTI 2012,
KITTI 2015 and MPI-Sintel evaluation portals where we obtained state-of-the-art
results on all three datasets.
| Christian Bailer and Kiran Varanasi and Didier Stricker | null | 1607.08064 | null | null |
Improving Semantic Embedding Consistency by Metric Learning for
Zero-Shot Classification | cs.CV cs.AI cs.LG math.ST stat.TH | This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.
| Maxime Bucher (Palaiseau), St\'ephane Herbin (Palaiseau), Fr\'ed\'eric
Jurie | null | 1607.08085 | null | null |
Network-Guided Biomarker Discovery | stat.ML cs.LG q-bio.QM | Identifying measurable genetic indicators (or biomarkers) of a specific
condition of a biological system is a key element of precision medicine. Indeed
it allows to tailor diagnostic, prognostic and treatment choice to individual
characteristics of a patient. In machine learning terms, biomarker discovery
can be framed as a feature selection problem on whole-genome data sets.
However, classical feature selection methods are usually underpowered to
process these data sets, which contain orders of magnitude more features than
samples. This can be addressed by making the assumption that genetic features
that are linked on a biological network are more likely to work jointly towards
explaining the phenotype of interest. We review here three families of methods
for feature selection that integrate prior knowledge in the form of networks.
| Chlo\'e-Agathe Azencott | 10.1007/978-3-319-50478-0_16 | 1607.08161 | null | null |
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding | stat.ML cs.LG | Convolutional neural networks (CNN) have led to many state-of-the-art results
spanning through various fields. However, a clear and profound theoretical
understanding of the forward pass, the core algorithm of CNN, is still lacking.
In parallel, within the wide field of sparse approximation, Convolutional
Sparse Coding (CSC) has gained increasing attention in recent years. A
theoretical study of this model was recently conducted, establishing it as a
reliable and stable alternative to the commonly practiced patch-based
processing. Herein, we propose a novel multi-layer model, ML-CSC, in which
signals are assumed to emerge from a cascade of CSC layers. This is shown to be
tightly connected to CNN, so much so that the forward pass of the CNN is in
fact the thresholding pursuit serving the ML-CSC model. This connection brings
a fresh view to CNN, as we are able to attribute to this architecture
theoretical claims such as uniqueness of the representations throughout the
network, and their stable estimation, all guaranteed under simple local
sparsity conditions. Lastly, identifying the weaknesses in the above pursuit
scheme, we propose an alternative to the forward pass, which is connected to
deconvolutional, recurrent and residual networks, and has better theoretical
guarantees.
| Vardan Papyan, Yaniv Romano and Michael Elad | null | 1607.08194 | null | null |
Diagnostic Prediction Using Discomfort Drawings with IBTM | cs.LG | In this paper, we explore the possibility to apply machine learning to make
diagnostic predictions using discomfort drawings. A discomfort drawing is an
intuitive way for patients to express discomfort and pain related symptoms.
These drawings have proven to be an effective method to collect patient data
and make diagnostic decisions in real-life practice. A dataset from real-world
patient cases is collected for which medical experts provide diagnostic labels.
Next, we use a factorized multimodal topic model, Inter-Battery Topic Model
(IBTM), to train a system that can make diagnostic predictions given an unseen
discomfort drawing. The number of output diagnostic labels is determined by
using mean-shift clustering on the discomfort drawing. Experimental results
show reasonable predictions of diagnostic labels given an unseen discomfort
drawing. Additionally, we generate synthetic discomfort drawings with IBTM
given a diagnostic label, which results in typical cases of symptoms. The
positive result indicates a significant potential of machine learning to be
used for parts of the pain diagnostic process and to be a decision support
system for physicians and other health care personnel.
| Cheng Zhang, Hedvig Kjellstrom, Carl Henrik Ek, Bo C. Bertilson | null | 1607.08206 | null | null |
Stochastic Frank-Wolfe Methods for Nonconvex Optimization | math.OC cs.LG stat.ML | We study Frank-Wolfe methods for nonconvex stochastic and finite-sum
optimization problems. Frank-Wolfe methods (in the convex case) have gained
tremendous recent interest in machine learning and optimization communities due
to their projection-free property and their ability to exploit structured
constraints. However, our understanding of these algorithms in the nonconvex
setting is fairly limited. In this paper, we propose nonconvex stochastic
Frank-Wolfe methods and analyze their convergence properties. For objective
functions that decompose into a finite-sum, we leverage ideas from variance
reduction techniques for convex optimization to obtain new variance reduced
nonconvex Frank-Wolfe methods that have provably faster convergence than the
classical Frank-Wolfe method. Finally, we show that the faster convergence
rates of our variance reduced methods also translate into improved convergence
rates for the stochastic setting.
| Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola | null | 1607.08254 | null | null |
Mammalian Value Systems | cs.AI cs.CY cs.HC cs.LG cs.RO | Characterizing human values is a topic deeply interwoven with the sciences,
humanities, art, and many other human endeavors. In recent years, a number of
thinkers have argued that accelerating trends in computer science, cognitive
science, and related disciplines foreshadow the creation of intelligent
machines which meet and ultimately surpass the cognitive abilities of human
beings, thereby entangling an understanding of human values with future
technological development. Contemporary research accomplishments suggest
sophisticated AI systems becoming widespread and responsible for managing many
aspects of the modern world, from preemptively planning users' travel schedules
and logistics, to fully autonomous vehicles, to domestic robots assisting in
daily living. The extrapolation of these trends has been most forcefully
described in the context of a hypothetical "intelligence explosion," in which
the capabilities of an intelligent software agent would rapidly increase due to
the presence of feedback loops unavailable to biological organisms. The
possibility of superintelligent agents, or simply the widespread deployment of
sophisticated, autonomous AI systems, highlights an important theoretical
problem: the need to separate the cognitive and rational capacities of an agent
from the fundamental goal structure, or value system, which constrains and
guides the agent's actions. The "value alignment problem" is to specify a goal
structure for autonomous agents compatible with human values. In this brief
article, we suggest that recent ideas from affective neuroscience and related
disciplines aimed at characterizing neurological and behavioral universals in
the mammalian class provide important conceptual foundations relevant to
describing human values. We argue that the notion of "mammalian value systems"
points to a potential avenue for fundamental research in AI safety and AI
ethics.
| Gopal P. Sarma and Nick J. Hay | null | 1607.08289 | null | null |
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using
Deterministic RBF Surrogates | cs.AI cs.LG stat.ML | Automatically searching for optimal hyperparameter configurations is of
crucial importance for applying deep learning algorithms in practice. Recently,
Bayesian optimization has been proposed for optimizing hyperparameters of
various machine learning algorithms. Those methods adopt probabilistic
surrogate models like Gaussian processes to approximate and minimize the
validation error function of hyperparameter values. However, probabilistic
surrogates require accurate estimates of sufficient statistics (e.g.,
covariance) of the error distribution and thus need many function evaluations
with a sizeable number of hyperparameters. This makes them inefficient for
optimizing hyperparameters of deep learning algorithms, which are highly
expensive to evaluate. In this work, we propose a new deterministic and
efficient hyperparameter optimization method that employs radial basis
functions as error surrogates. The proposed mixed integer algorithm, called
HORD, searches the surrogate for the most promising hyperparameter values
through dynamic coordinate search and requires many fewer function evaluations.
HORD does well in low dimensions but it is exceptionally better in higher
dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural
networks demonstrate HORD significantly outperforms the well-established
Bayesian optimization methods such as GP, SMAC, and TPE. For instance, on
average, HORD is more than 6 times faster than GP-EI in obtaining the best
configuration of 19 hyperparameters.
| Ilija Ilievski and Taimoor Akhtar and Jiashi Feng and Christine
Annette Shoemaker | null | 1607.08316 | null | null |
Randomised Algorithm for Feature Selection and Classification | cs.LG | We here introduce a novel classification approach adopted from the nonlinear
model identification framework, which jointly addresses the feature selection
and classifier design tasks. The classifier is constructed as a polynomial
expansion of the original attributes and a model structure selection process is
applied to find the relevant terms of the model. The selection method
progressively refines a probability distribution defined on the model structure
space, by extracting sample models from the current distribution and using the
aggregate information obtained from the evaluation of the population of models
to reinforce the probability of extracting the most important terms. To reduce
the initial search space, distance correlation filtering can be applied as a
preprocessing technique. The proposed method is evaluated and compared to other
well-known feature selection and classification methods on standard benchmark
classification problems. The results show the effectiveness of the proposed
method with respect to competitor methods both in terms of classification
accuracy and model complexity. The obtained models have a simple structure,
easily amenable to interpretation and analysis.
| Aida Brankovic, Alessandro Falsone, Maria Prandini, Luigi Piroddi | null | 1607.084 | null | null |
Kernel functions based on triplet comparisons | stat.ML cs.DS cs.LG | Given only information in the form of similarity triplets "Object A is more
similar to object B than to object C" about a data set, we propose two ways of
defining a kernel function on the data set. While previous approaches construct
a low-dimensional Euclidean embedding of the data set that reflects the given
similarity triplets, we aim at defining kernel functions that correspond to
high-dimensional embeddings. These kernel functions can subsequently be used to
apply any kernel method to the data set.
| Matth\"aus Kleindessner and Ulrike von Luxburg | null | 1607.08456 | null | null |
Attribute Learning for Network Intrusion Detection | cs.CR cs.LG | Network intrusion detection is one of the most visible uses for Big Data
analytics. One of the main problems in this application is the constant rise of
new attacks. This scenario, characterized by the fact that not enough labeled
examples are available for the new classes of attacks is hardly addressed by
traditional machine learning approaches. New findings on the capabilities of
Zero-Shot learning (ZSL) approach makes it an interesting solution for this
problem because it has the ability to classify instances of unseen classes. ZSL
has inherently two stages: the attribute learning and the inference stage. In
this paper we propose a new algorithm for the attribute learning stage of ZSL.
The idea is to learn new values for the attributes based on decision trees
(DT). Our results show that based on the rules extracted from the DT a better
distribution for the attribute values can be found. We also propose an
experimental setup for the evaluation of ZSL on network intrusion detection
(NID).
| Jorge Luis Rivero P\'erez and Bernardete Ribeiro | null | 1607.08634 | null | null |
A Non-Parametric Learning Approach to Identify Online Human Trafficking | cs.LG stat.ML | Human trafficking is among the most challenging law enforcement problems
which demands persistent fight against from all over the globe. In this study,
we leverage readily available data from the website "Backpage"-- used for
classified advertisement-- to discern potential patterns of human trafficking
activities which manifest online and identify most likely trafficking related
advertisements. Due to the lack of ground truth, we rely on two human analysts
--one human trafficking victim survivor and one from law enforcement, for
hand-labeling the small portion of the crawled data. We then present a
semi-supervised learning approach that is trained on the available labeled and
unlabeled data and evaluated on unseen data with further verification of
experts.
| Hamidreza Alvari, Paulo Shakarian, J.E. Kelly Snyder | null | 1607.08691 | null | null |
TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for
Automatic Measurement in MOOCs | cs.LG cs.CL cs.IR stat.ML | This paper explores the suitability of using automatically discovered topics
from MOOC discussion forums for modelling students' academic abilities. The
Rasch model from psychometrics is a popular generative probabilistic model that
relates latent student skill, latent item difficulty, and observed student-item
responses within a principled, unified framework. According to scholarly
educational theory, discovered topics can be regarded as appropriate
measurement items if (1) students' participation across the discovered topics
is well fit by the Rasch model, and if (2) the topics are interpretable to
subject-matter experts as being educationally meaningful. Such Rasch-scaled
topics, with associated difficulty levels, could be of potential benefit to
curriculum refinement, student assessment and personalised feedback. The
technical challenge that remains, is to discover meaningful topics that
simultaneously achieve good statistical fit with the Rasch model. To address
this challenge, we combine the Rasch model with non-negative matrix
factorisation based topic modelling, jointly fitting both models. We
demonstrate the suitability of our approach with quantitative experiments on
data from three Coursera MOOCs, and with qualitative survey results on topic
interpretability on a Discrete Optimisation MOOC.
| Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra
Milligan | null | 1607.0872 | null | null |
Cognitive Science in the era of Artificial Intelligence: A roadmap for
reverse-engineering the infant language-learner | cs.CL cs.AI cs.LG | During their first years of life, infants learn the language(s) of their
environment at an amazing speed despite large cross cultural variations in
amount and complexity of the available language input. Understanding this
simple fact still escapes current cognitive and linguistic theories. Recently,
spectacular progress in the engineering science, notably, machine learning and
wearable technology, offer the promise of revolutionizing the study of
cognitive development. Machine learning offers powerful learning algorithms
that can achieve human-like performance on many linguistic tasks. Wearable
sensors can capture vast amounts of data, which enable the reconstruction of
the sensory experience of infants in their natural environment. The project of
'reverse engineering' language development, i.e., of building an effective
system that mimics infant's achievements appears therefore to be within reach.
Here, we analyze the conditions under which such a project can contribute to
our scientific understanding of early language development. We argue that
instead of defining a sub-problem or simplifying the data, computational models
should address the full complexity of the learning situation, and take as input
the raw sensory signals available to infants. This implies that (1) accessible
but privacy-preserving repositories of home data be setup and widely shared,
and (2) models be evaluated at different linguistic levels through a benchmark
of psycholinguist tests that can be passed by machines and humans alike, (3)
linguistically and psychologically plausible learning architectures be scaled
up to real data using probabilistic/optimization principles from machine
learning. We discuss the feasibility of this approach and present preliminary
results.
| Emmanuel Dupoux | 10.1016/j.cognition.2017.11.008 | 1607.08723 | null | null |
Polynomial Networks and Factorization Machines: New Insights and
Efficient Training Algorithms | stat.ML cs.LG | Polynomial networks and factorization machines are two recently-proposed
models that can efficiently use feature interactions in classification and
regression tasks. In this paper, we revisit both models from a unified
perspective. Based on this new view, we study the properties of both models and
propose new efficient training algorithms. Key to our approach is to cast
parameter learning as a low-rank symmetric tensor estimation problem, which we
solve by multi-convex optimization. We demonstrate our approach on regression
and recommender system tasks.
| Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda | null | 1607.0881 | null | null |
Exponentially fast convergence to (strict) equilibrium via hedging | cs.GT cs.LG math.OC | Motivated by applications to data networks where fast convergence is
essential, we analyze the problem of learning in generic N-person games that
admit a Nash equilibrium in pure strategies. Specifically, we consider a
scenario where players interact repeatedly and try to learn from past
experience by small adjustments based on local - and possibly imperfect -
payoff information. For concreteness, we focus on the so-called "hedge" variant
of the exponential weights algorithm where players select an action with
probability proportional to the exponential of the action's cumulative payoff
over time. When players have perfect information on their mixed payoffs, the
algorithm converges locally to a strict equilibrium and the rate of convergence
is exponentially fast - of the order of
$\mathcal{O}(\exp(-a\sum_{j=1}^{t}\gamma_{j}))$ where $a>0$ is a constant and
$\gamma_{j}$ is the algorithm's step-size. In the presence of uncertainty,
convergence requires a more conservative step-size policy, but with high
probability, the algorithm remains locally convergent and achieves an
exponential convergence rate.
| Johanne Cohen and Am\'elie H\'eliou and Panayotis Mertikopoulos | null | 1607.08863 | null | null |
Identifying and Harnessing the Building Blocks of Machine Learning
Pipelines for Sensible Initialization of a Data Science Automation Tool | cs.NE cs.AI cs.LG | As data science continues to grow in popularity, there will be an increasing
need to make data science tools more scalable, flexible, and accessible. In
particular, automated machine learning (AutoML) systems seek to automate the
process of designing and optimizing machine learning pipelines. In this
chapter, we present a genetic programming-based AutoML system called TPOT that
optimizes a series of feature preprocessors and machine learning models with
the goal of maximizing classification accuracy on a supervised classification
problem. Further, we analyze a large database of pipelines that were previously
used to solve various supervised classification problems and identify 100 short
series of machine learning operations that appear the most frequently, which we
call the building blocks of machine learning pipelines. We harness these
building blocks to initialize TPOT with promising solutions, and find that this
sensible initialization method significantly improves TPOT's performance on one
benchmark at no cost of significantly degrading performance on the others.
Thus, sensible initialization with machine learning pipeline building blocks
shows promise for GP-based AutoML systems, and should be further refined in
future work.
| Randal S. Olson and Jason H. Moore | null | 1607.08878 | null | null |
gLOP: the global and Local Penalty for Capturing Predictive
Heterogeneity | stat.ML cs.LG | When faced with a supervised learning problem, we hope to have rich enough
data to build a model that predicts future instances well. However, in
practice, problems can exhibit predictive heterogeneity: most instances might
be relatively easy to predict, while others might be predictive outliers for
which a model trained on the entire dataset does not perform well. Identifying
these can help focus future data collection. We present gLOP, the global and
Local Penalty, a framework for capturing predictive heterogeneity and
identifying predictive outliers. gLOP is based on penalized regression for
multitask learning, which improves learning by leveraging training signal
information from related tasks. We give two optimization algorithms for gLOP,
one space-efficient, and another giving the full regularization path. We also
characterize uniqueness in terms of the data and tuning parameters, and present
empirical results on synthetic data and on two health research problems.
| Rhiannon V. Rose, Daniel J. Lizotte | null | 1608.00027 | null | null |
Online Learning of Event Definitions | cs.LG cs.AI | Systems for symbolic event recognition infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Inductive Logic Programming (ILP). We present an ILP system for online
learning of Event Calculus theories. To allow for a single-pass learning
strategy, we use the Hoeffding bound for evaluating clauses on a subset of the
input stream. We employ a decoupling scheme of the Event Calculus axioms during
the learning process, that allows to learn each clause in isolation. Moreover,
we use abductive-inductive logic programming techniques to handle unobserved
target predicates. We evaluate our approach on an activity recognition
application and compare it to a number of batch learning techniques. We obtain
results of comparable predicative accuracy with significant speed-ups in
training time. We also outperform hand-crafted rules and match the performance
of a sound incremental learner that can only operate on noise-free datasets.
This paper is under consideration for acceptance in TPLP.
| Nikos Katzouris, Alexander Artikis, Georgios Paliouras | 10.1017/S1471068416000260 | 1608.001 | null | null |
World Knowledge as Indirect Supervision for Document Clustering | cs.LG cs.CL cs.IR | One of the key obstacles in making learning protocols realistic in
applications is the need to supervise them, a costly process that often
requires hiring domain experts. We consider the framework to use the world
knowledge as indirect supervision. World knowledge is general-purpose
knowledge, which is not designed for any specific domain. Then the key
challenges are how to adapt the world knowledge to domains and how to represent
it for learning. In this paper, we provide an example of using world knowledge
for domain dependent document clustering. We provide three ways to specify the
world knowledge to domains by resolving the ambiguity of the entities and their
types, and represent the data with world knowledge as a heterogeneous
information network. Then we propose a clustering algorithm that can cluster
multiple types and incorporate the sub-type information as constraints. In the
experiments, we use two existing knowledge bases as our sources of world
knowledge. One is Freebase, which is collaboratively collected knowledge about
entities and their organizations. The other is YAGO2, a knowledge base
automatically extracted from Wikipedia and maps knowledge to the linguistic
knowledge base, WordNet. Experimental results on two text benchmark datasets
(20newsgroups and RCV1) show that incorporating world knowledge as indirect
supervision can significantly outperform the state-of-the-art clustering
algorithms as well as clustering algorithms enhanced with world knowledge
features.
| Chenguang Wang, Yangqiu Song, Dan Roth, Ming Zhang, Jiawei Han | null | 1608.00104 | null | null |
Learning Tree-Structured Detection Cascades for Heterogeneous Networks
of Embedded Devices | stat.ML cs.LG | In this paper, we present a new approach to learning cascaded classifiers for
use in computing environments that involve networks of heterogeneous and
resource-constrained, low-power embedded compute and sensing nodes. We present
a generalization of the classical linear detection cascade to the case of
tree-structured cascades where different branches of the tree execute on
different physical compute nodes in the network. Different nodes have access to
different features, as well as access to potentially different computation and
energy resources. We concentrate on the problem of jointly learning the
parameters for all of the classifiers in the cascade given a fixed cascade
architecture and a known set of costs required to carry out the computation at
each node.To accomplish the objective of joint learning of all detectors, we
propose a novel approach to combining classifier outputs during training that
better matches the hard cascade setting in which the learned system will be
deployed. This work is motivated by research in the area of mobile health where
energy efficient real time detectors integrating information from multiple
wireless on-body sensors and a smart phone are needed for real-time monitoring
and delivering just- in-time adaptive interventions. We apply our framework to
two activity recognition datasets as well as the problem of cigarette smoking
detection from a combination of wrist-worn actigraphy data and respiration
chest band data.
| Hamid Dadkhahi and Benjamin M. Marlin | 10.1145/3097983.3098169 | 1608.00159 | null | null |
Deep FisherNet for Object Classification | cs.CV cs.LG | Despite the great success of convolutional neural networks (CNN) for the
image classification task on datasets like Cifar and ImageNet, CNN's
representation power is still somewhat limited in dealing with object images
that have large variation in size and clutter, where Fisher Vector (FV) has
shown to be an effective encoding strategy. FV encodes an image by aggregating
local descriptors with a universal generative Gaussian Mixture Model (GMM). FV
however has limited learning capability and its parameters are mostly fixed
after constructing the codebook. To combine together the best of the two
worlds, we propose in this paper a neural network structure with FV layer being
part of an end-to-end trainable system that is differentiable; we name our
network FisherNet that is learnable using backpropagation. Our proposed
FisherNet combines convolutional neural network training and Fisher Vector
encoding in a single end-to-end structure. We observe a clear advantage of
FisherNet over plain CNN and standard FV in terms of both classification
accuracy and computational efficiency on the challenging PASCAL VOC object
classification task.
| Peng Tang, Xinggang Wang, Baoguang Shi, Xiang Bai, Wenyu Liu, Zhuowen
Tu | null | 1608.00182 | null | null |
Hyperparameter Transfer Learning through Surrogate Alignment for
Efficient Deep Neural Network Training | cs.LG cs.CV cs.NE stat.ML | Recently, several optimization methods have been successfully applied to the
hyperparameter optimization of deep neural networks (DNNs). The methods work by
modeling the joint distribution of hyperparameter values and corresponding
error. Those methods become less practical when applied to modern DNNs whose
training may take a few days and thus one cannot collect sufficient
observations to accurately model the distribution. To address this challenging
issue, we propose a method that learns to transfer optimal hyperparameter
values for a small source dataset to hyperparameter values with comparable
performance on a dataset of interest. As opposed to existing transfer learning
methods, our proposed method does not use hand-designed features. Instead, it
uses surrogates to model the hyperparameter-error distributions of the two
datasets and trains a neural network to learn the transfer function. Extensive
experiments on three CV benchmark datasets clearly demonstrate the efficiency
of our method.
| Ilija Ilievski and Jiashi Feng | null | 1608.00218 | null | null |
Learning Robust Features using Deep Learning for Automatic Seizure
Detection | cs.LG cs.CV | We present and evaluate the capacity of a deep neural network to learn robust
features from EEG to automatically detect seizures. This is a challenging
problem because seizure manifestations on EEG are extremely variable both
inter- and intra-patient. By simultaneously capturing spectral, temporal and
spatial information our recurrent convolutional neural network learns a general
spatially invariant representation of a seizure. The proposed approach exceeds
significantly previous results obtained on cross-patient classifiers both in
terms of sensitivity and false positive rate. Furthermore, our model proves to
be robust to missing channel and variable electrode montage.
| Pierre Thodoroff, Joelle Pineau, Andrew Lim | null | 1608.0022 | null | null |
Input-Output Non-Linear Dynamical Systems applied to Physiological
Condition Monitoring | cs.LG | We present a non-linear dynamical system for modelling the effect of drug
infusions on the vital signs of patients admitted in Intensive Care Units
(ICUs). More specifically we are interested in modelling the effect of a widely
used anaesthetic drug (Propofol) on a patient's monitored depth of anaesthesia
and haemodynamics. We compare our approach with one from the
Pharmacokinetics/Pharmacodynamics (PK/PD) literature and show that we can
provide significant improvements in performance without requiring the
incorporation of expert physiological knowledge in our system.
| Konstantinos Georgatzis, Christopher K. I. Williams, Christopher
Hawthorne | null | 1608.00242 | null | null |
On Regularization Parameter Estimation under Covariate Shift | cs.LG stat.ML | This paper identifies a problem with the usual procedure for
L2-regularization parameter estimation in a domain adaptation setting. In such
a setting, there are differences between the distributions generating the
training data (source domain) and the test data (target domain). The usual
cross-validation procedure requires validation data, which can not be obtained
from the unlabeled target data. The problem is that if one decides to use
source validation data, the regularization parameter is underestimated. One
possible solution is to scale the source validation data through importance
weighting, but we show that this correction is not sufficient. We conclude the
paper with an empirical analysis of the effect of several importance weight
estimators on the estimation of the regularization parameter.
| Wouter M. Kouw and Marco Loog | 10.1109/ICPR.2016.7899671 | 1608.0025 | null | null |
A Neural Knowledge Language Model | cs.CL cs.LG | Current language models have a significant limitation in the ability to
encode and decode factual knowledge. This is mainly because they acquire such
knowledge from statistical co-occurrences although most of the knowledge words
are rarely observed. In this paper, we propose a Neural Knowledge Language
Model (NKLM) which combines symbolic knowledge provided by the knowledge graph
with the RNN language model. By predicting whether the word to generate has an
underlying fact or not, the model can generate such knowledge-related words by
copying from the description of the predicted fact. In experiments, we show
that the NKLM significantly improves the performance while generating a much
smaller number of unknown words.
| Sungjin Ahn, Heeyoul Choi, Tanel P\"arnamaa, Yoshua Bengio | null | 1608.00318 | null | null |
Discovering Latent States for Model Learning: Applying Sensorimotor
Contingencies Theory and Predictive Processing to Model Context | cs.RO cs.AI cs.LG | Autonomous robots need to be able to adapt to unforeseen situations and to
acquire new skills through trial and error. Reinforcement learning in principle
offers a suitable methodological framework for this kind of autonomous
learning. However current computational reinforcement learning agents mostly
learn each individual skill entirely from scratch. How can we enable artificial
agents, such as robots, to acquire some form of generic knowledge, which they
could leverage for the learning of new skills? This paper argues that, like the
brain, the cognitive system of artificial agents has to develop a world model
to support adaptive behavior and learning. Inspiration is taken from two recent
developments in the cognitive science literature: predictive processing
theories of cognition, and the sensorimotor contingencies theory of perception.
Based on these, a hypothesis is formulated about what the content of
information might be that is encoded in an internal world model, and how an
agent could autonomously acquire it. A computational model is described to
formalize this hypothesis, and is evaluated in a series of simulation
experiments.
| Nikolas J. Hemion | null | 1608.00359 | null | null |
Learning Semantically Coherent and Reusable Kernels in Convolution
Neural Nets for Sentence Classification | cs.CL cs.LG cs.NE | The state-of-the-art CNN models give good performance on sentence
classification tasks. The purpose of this work is to empirically study
desirable properties such as semantic coherence, attention mechanism and
reusability of CNNs in these tasks. Semantically coherent kernels are
preferable as they are a lot more interpretable for explaining the decision of
the learned CNN model. We observe that the learned kernels do not have semantic
coherence. Motivated by this observation, we propose to learn kernels with
semantic coherence using clustering scheme combined with Word2Vec
representation and domain knowledge such as SentiWordNet. We suggest a
technique to visualize attention mechanism of CNNs for decision explanation
purpose. Reusable property enables kernels learned on one problem to be used in
another problem. This helps in efficient learning as only a few additional
domain specific filters may have to be learned. We demonstrate the efficacy of
our core ideas of learning semantically coherent kernels and leveraging
reusable kernels for efficient learning on several benchmark datasets.
Experimental results show the usefulness of our approach by achieving
performance close to the state-of-the-art methods but with semantic and
reusable properties.
| Madhusudan Lakshmana, Sundararajan Sellamanickam, Shirish Shevade,
Keerthi Selvaraj | null | 1608.00466 | null | null |
Early Methods for Detecting Adversarial Images | cs.LG cs.CR cs.CV cs.NE | Many machine learning classifiers are vulnerable to adversarial
perturbations. An adversarial perturbation modifies an input to change a
classifier's prediction without causing the input to seem substantially
different to human perception. We deploy three methods to detect adversarial
images. Adversaries trying to bypass our detectors must make the adversarial
image less pathological or they will fail trying. Our best detection method
reveals that adversarial images place abnormal emphasis on the lower-ranked
principal components from PCA. Other detectors and a colorful saliency map are
in an appendix.
| Dan Hendrycks, Kevin Gimpel | null | 1608.0053 | null | null |
Theory of the GMM Kernel | stat.ME cs.DS cs.IT cs.LG math.IT | We develop some theoretical results for a robust similarity measure named
"generalized min-max" (GMM). This similarity has direct applications in machine
learning as a positive definite kernel and can be efficiently computed via
probabilistic hashing. Owing to the discrete nature, the hashed values can also
be used for efficient near neighbor search. We prove the theoretical limit of
GMM and the consistency result, assuming that the data follow an elliptical
distribution, which is a very general family of distributions and includes the
multivariate $t$-distribution as a special case. The consistency result holds
as long as the data have bounded first moment (an assumption which essentially
holds for datasets commonly encountered in practice). Furthermore, we establish
the asymptotic normality of GMM. Compared to the "cosine" similarity which is
routinely adopted in current practice in statistics and machine learning, the
consistency of GMM requires much weaker conditions. Interestingly, when the
data follow the $t$-distribution with $\nu$ degrees of freedom, GMM typically
provides a better measure of similarity than "cosine" roughly when $\nu<8$
(which is already very close to normal). These theoretical results will help
explain the recent success of GMM in learning tasks.
| Ping Li and Cun-Hui Zhang | null | 1608.0055 | null | null |
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale
Image Recognition | cs.CV cs.LG cs.NE | One of the key challenges in machine learning is to design a computationally
efficient multi-class classifier while maintaining the output accuracy and
performance. In this paper, we present a tree-based classifier: Attention Tree
(ATree) for large-scale image classification that uses recursive Adaboost
training to construct a visual attention hierarchy. The proposed attention
model is inspired from the biological 'selective tuning mechanism for cortical
visual processing'. We exploit the inherent feature similarity across images in
datasets to identify the input variability and use recursive optimization
procedure, to determine data partitioning at each node, thereby, learning the
attention hierarchy. A set of binary classifiers is organized on top of the
learnt hierarchy to minimize the overall test-time complexity. The attention
model maximizes the margins for the binary classifiers for optimal decision
boundary modelling, leading to better performance at minimal complexity. The
proposed framework has been evaluated on both Caltech-256 and SUN datasets and
achieves accuracy improvement over state-of-the-art tree-based methods at
significantly lower computational cost.
| Priyadarshini Panda, and Kaushik Roy | null | 1608.00611 | null | null |
Recursion-Free Online Multiple Incremental/Decremental Analysis Based on
Ridge Support Vector Learning | cs.LG stat.ML | This study presents a rapid multiple incremental and decremental mechanism
based on Weight-Error Curves (WECs) for support-vector analysis. Recursion-free
computation is proposed for predicting the Lagrangian multipliers of new
samples. This study examines Ridge Support Vector Models, subsequently devising
a recursion-free function derived from WECs. With the proposed function, all
the new Lagrangian multipliers can be computed at once without using any
gradual step sizes. Moreover, such a function relaxes a constraint, where the
increment of new multiple Lagrangian multipliers should be the same in the
previous work, thereby easily satisfying the requirement of KKT conditions. The
proposed mechanism no longer requires typical bookkeeping strategies, which
compute the step size by checking all the training samples in each incremental
round.
| Bo-Wei Chen | null | 1608.00619 | null | null |
Efficient Multiple Incremental Computation for Kernel Ridge Regression
with Bayesian Uncertainty Modeling | cs.LG stat.ML | This study presents an efficient incremental/decremental approach for big
streams based on Kernel Ridge Regression (KRR), a frequently used data analysis
in cloud centers. To avoid reanalyzing the whole dataset whenever sensors
receive new training data, typical incremental KRR used a single-instance
mechanism for updating an existing system. However, this inevitably increased
redundant computational time, not to mention applicability to big streams. To
this end, the proposed mechanism supports incremental/decremental processing
for both single and multiple samples (i.e., batch processing). A large scale of
data can be divided into batches, processed by a machine, without sacrificing
the accuracy. Moreover, incremental/decremental analyses in empirical and
intrinsic space are also proposed in this study to handle different types of
data either with a large number of samples or high feature dimensions, whereas
typical methods focused only on one type. At the end of this study, we further
the proposed mechanism to statistical Kernelized Bayesian Regression, so that
uncertainty modeling with incremental/decremental computation becomes
applicable. Experimental results showed that computational time was
significantly reduced, better than the original nonincremental design and the
typical single incremental method. Furthermore, the accuracy of the proposed
method remained the same as the baselines. This implied that the system
enhanced efficiency without sacrificing the accuracy. These findings proved
that the proposed method was appropriate for variable streaming data analysis,
thereby demonstrating the effectiveness of the proposed method.
| Bo-Wei Chen, Nik Nailah Binti Abdullah, and Sangoh Park | 10.1016/j.future.2017.08.053 | 1608.00621 | null | null |
Learning Transferable Policies for Monocular Reactive MAV Control | cs.RO cs.AI cs.LG | The ability to transfer knowledge gained in previous tasks into new contexts
is one of the most important mechanisms of human learning. Despite this,
adapting autonomous behavior to be reused in partially similar settings is
still an open problem in current robotics research. In this paper, we take a
small step in this direction and propose a generic framework for learning
transferable motion policies. Our goal is to solve a learning problem in a
target domain by utilizing the training data in a different but related source
domain. We present this in the context of an autonomous MAV flight using
monocular reactive control, and demonstrate the efficacy of our proposed
approach through extensive real-world flight experiments in outdoor cluttered
environments.
| Shreyansh Daftry, J. Andrew Bagnell and Martial Hebert | null | 1608.00627 | null | null |
Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests | cs.LG | Disparate areas of machine learning have benefited from models that can take
raw data with little preprocessing as input and learn rich representations of
that raw data in order to perform well on a given prediction task. We evaluate
this approach in healthcare by using longitudinal measurements of lab tests,
one of the more raw signals of a patient's health state widely available in
clinical data, to predict disease onsets. In particular, we train a Long
Short-Term Memory (LSTM) recurrent neural network and two novel convolutional
neural networks for multi-task prediction of disease onset for 133 conditions
based on 18 common lab tests measured over time in a cohort of 298K patients
derived from 8 years of administrative claims data. We compare the neural
networks to a logistic regression with several hand-engineered, clinically
relevant features. We find that the representation-based learning approaches
significantly outperform this baseline. We believe that our work suggests a new
avenue for patient risk stratification based solely on lab results.
| Narges Razavian, Jake Marcus, David Sontag | null | 1608.00647 | null | null |
Can Active Learning Experience Be Transferred? | cs.LG cs.AI | Active learning is an important machine learning problem in reducing the
human labeling effort. Current active learning strategies are designed from
human knowledge, and are applied on each dataset in an immutable manner. In
other words, experience about the usefulness of strategies cannot be updated
and transferred to improve active learning on other datasets. This paper
initiates a pioneering study on whether active learning experience can be
transferred. We first propose a novel active learning model that linearly
aggregates existing strategies. The linear weights can then be used to
represent the active learning experience. We equip the model with the popular
linear upper- confidence-bound (LinUCB) algorithm for contextual bandit to
update the weights. Finally, we extend our model to transfer the experience
across datasets with the technique of biased regularization. Empirical studies
demonstrate that the learned experience not only is competitive with existing
strategies on most single datasets, but also can be transferred across datasets
to improve the performance on future learning tasks.
| Hong-Min Chu, Hsuan-Tien Lin | null | 1608.00667 | null | null |
Clinical Tagging with Joint Probabilistic Models | stat.ML cs.LG | We describe a method for parameter estimation in bipartite probabilistic
graphical models for joint prediction of clinical conditions from the
electronic medical record. The method does not rely on the availability of
gold-standard labels, but rather uses noisy labels, called anchors, for
learning. We provide a likelihood-based objective and a moments-based
initialization that are effective at learning the model parameters. The learned
model is evaluated in a task of assigning a heldout clinical condition to
patients based on retrospective analysis of the records, and outperforms
baselines which do not account for the noisiness in the labels or do not model
the conditions jointly.
| Yoni Halpern and Steven Horng and David Sontag | null | 1608.00686 | null | null |
Identifiable Phenotyping using Constrained Non-Negative Matrix
Factorization | stat.ML cs.LG | This work proposes a new algorithm for automated and simultaneous phenotyping
of multiple co-occurring medical conditions, also referred as comorbidities,
using clinical notes from the electronic health records (EHRs). A basic latent
factor estimation technique of non-negative matrix factorization (NMF) is
augmented with domain specific constraints to obtain sparse latent factors that
are anchored to a fixed set of chronic conditions. The proposed anchoring
mechanism ensures a one-to-one identifiable and interpretable mapping between
the latent factors and the target comorbidities. Qualitative assessment of the
empirical results by clinical experts suggests that the proposed model learns
clinically interpretable phenotypes while being predictive of 30 day mortality.
The proposed method can be readily adapted to any non-negative EHR data across
various healthcare institutions.
| Shalmali Joshi, Suriya Gunasekar, David Sontag, Joydeep Ghosh | null | 1608.00704 | null | null |
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks | cs.LG | An important task in network analysis is the detection of anomalous events in
a network time series. These events could merely be times of interest in the
network timeline or they could be examples of malicious activity or network
malfunction. Hypothesis testing using network statistics to summarize the
behavior of the network provides a robust framework for the anomaly detection
decision process. Unfortunately, choosing network statistics that are dependent
on confounding factors like the total number of nodes or edges can lead to
incorrect conclusions (e.g., false positives and false negatives). In this
dissertation we describe the challenges that face anomaly detection in dynamic
network streams regarding confounding factors. We also provide two solutions to
avoiding error due to confounding factors: the first is a randomization testing
method that controls for confounding factors, and the second is a set of
size-consistent network statistics which avoid confounding due to the most
common factors, edge count and node count.
| Timothy La Fond, Jennifer Neville, Brian Gallagher | null | 1608.00712 | null | null |
Context Discovery for Model Learning in Partially Observable
Environments | cs.RO cs.AI cs.LG | The ability to learn a model is essential for the success of autonomous
agents. Unfortunately, learning a model is difficult in partially observable
environments, where latent environmental factors influence what the agent
observes. In the absence of a supervisory training signal, autonomous agents
therefore require a mechanism to autonomously discover these environmental
factors, or sensorimotor contexts.
This paper presents a method to discover sensorimotor contexts in partially
observable environments, by constructing a hierarchical transition model. The
method is evaluated in a simulation experiment, in which a robot learns that
different rooms are characterized by different objects that are found in them.
| Nikolas J. Hemion | null | 1608.00737 | null | null |
Exponential Family Embeddings | stat.ML cs.LG | Word embeddings are a powerful approach for capturing semantic similarity
among terms in a vocabulary. In this paper, we develop exponential family
embeddings, a class of methods that extends the idea of word embeddings to
other types of high-dimensional data. As examples, we studied neural data with
real-valued observations, count data from a market basket analysis, and ratings
data from a movie recommendation system. The main idea is to model each
observation conditioned on a set of other observations. This set is called the
context, and the way the context is defined is a modeling choice that depends
on the problem. In language the context is the surrounding words; in
neuroscience the context is close-by neurons; in market basket data the context
is other items in the shopping cart. Each type of embedding model defines the
context, the exponential family of conditional distributions, and how the
latent embedding vectors are shared across data. We infer the embeddings with a
scalable algorithm based on stochastic gradient descent. On all three
applications - neural activity of zebrafish, users' shopping behavior, and
movie ratings - we found exponential family embedding models to be more
effective than other types of dimension reduction. They better reconstruct
held-out data and find interesting qualitative structure.
| Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei | null | 1608.00778 | null | null |
Horn: A System for Parallel Training and Regularizing of Large-Scale
Neural Networks | cs.DC cs.LG cs.NE | I introduce a new distributed system for effective training and regularizing
of Large-Scale Neural Networks on distributed computing architectures. The
experiments demonstrate the effectiveness of flexible model partitioning and
parallelization strategies based on neuron-centric computation model, with an
implementation of the collective and parallel dropout neural networks training.
Experiments are performed on MNIST handwritten digits classification including
results.
| Edward J. Yoon | null | 1608.00781 | null | null |
High Accuracy Android Malware Detection Using Ensemble Learning | cs.CR cs.LG | With over 50 billion downloads and more than 1.3 million apps in the Google
official market, Android has continued to gain popularity amongst smartphone
users worldwide. At the same time there has been a rise in malware targeting
the platform, with more recent strains employing highly sophisticated detection
avoidance techniques. As traditional signature based methods become less potent
in detecting unknown malware, alternatives are needed for timely zero-day
discovery. Thus this paper proposes an approach that utilizes ensemble learning
for Android malware detection. It combines advantages of static analysis with
the efficiency and performance of ensemble machine learning to improve Android
malware detection accuracy. The machine learning models are built using a large
repository of malware samples and benign apps from a leading antivirus vendor.
Experimental results and analysis presented shows that the proposed method
which uses a large feature space to leverage the power of ensemble learning is
capable of 97.3 to 99 percent detection accuracy with very low false positive
rates.
| Suleiman Y. Yerima, Sakir Sezer, Igor Muttik | 10.1049/iet-ifs.2014.0099 | 1608.00835 | null | null |
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep
vs. Flat Feature Representations | cs.LG | Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for
understanding disease progression and for making informed treatment decisions.
New discoveries of significant alterations to mitochondria between subtypes
make immunohistochemical (IHC) staining based image classification an
imperative. Until now, accurate quantification and subtyping was made
impossible by huge IHC variations, the absence of cell membrane staining for
cytoplasm segmentation as well as the complete lack of systems for robust and
reproducible image based classification. In this paper we present a
comprehensive classification framework to overcome these challenges for tissue
microarrays (TMA) of RCCs. We compare and evaluate models based on domain
specific hand-crafted "flat"-features versus "deep" feature representations
from various layers of a pre-trained convolutional neural network (CNN). The
best model reaches a cross-validation accuracy of 89%, which demonstrates for
the first time, that robust mitochondria-based subtyping of renal cancer is
feasible
| Peter J. Sch\"uffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik,
Satish K. Tickoo, Thomas J. Fuchs | null | 1608.00842 | null | null |
A New Android Malware Detection Approach Using Bayesian Classification | cs.CR cs.LG | Mobile malware has been growing in scale and complexity as smartphone usage
continues to rise. Android has surpassed other mobile platforms as the most
popular whilst also witnessing a dramatic increase in malware targeting the
platform. A worrying trend that is emerging is the increasing sophistication of
Android malware to evade detection by traditional signature-based scanners. As
such, Android app marketplaces remain at risk of hosting malicious apps that
could evade detection before being downloaded by unsuspecting users. Hence, in
this paper we present an effective approach to alleviate this problem based on
Bayesian classification models obtained from static code analysis. The models
are built from a collection of code and app characteristics that provide
indicators of potential malicious activities. The models are evaluated with
real malware samples in the wild and results of experiments are presented to
demonstrate the effectiveness of the proposed approach.
| Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams, Igor Muttik | 10.1109/AINA.2013.88 | 1608.00848 | null | null |
A study of the effect of JPG compression on adversarial images | cs.CV cs.LG | Neural network image classifiers are known to be vulnerable to adversarial
images, i.e., natural images which have been modified by an adversarial
perturbation specifically designed to be imperceptible to humans yet fool the
classifier. Not only can adversarial images be generated easily, but these
images will often be adversarial for networks trained on disjoint subsets of
data or with different architectures. Adversarial images represent a potential
security risk as well as a serious machine learning challenge---it is clear
that vulnerable neural networks perceive images very differently from humans.
Noting that virtually every image classification data set is composed of JPG
images, we evaluate the effect of JPG compression on the classification of
adversarial images. For Fast-Gradient-Sign perturbations of small magnitude, we
found that JPG compression often reverses the drop in classification accuracy
to a large extent, but not always. As the magnitude of the perturbations
increases, JPG recompression alone is insufficient to reverse the effect.
| Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy | null | 1608.00853 | null | null |
Hierarchically Compositional Kernels for Scalable Nonparametric Learning | cs.LG stat.ML | We propose a novel class of kernels to alleviate the high computational cost
of large-scale nonparametric learning with kernel methods. The proposed kernel
is defined based on a hierarchical partitioning of the underlying data domain,
where the Nystr\"om method (a globally low-rank approximation) is married with
a locally lossless approximation in a hierarchical fashion. The kernel
maintains (strict) positive-definiteness. The corresponding kernel matrix
admits a recursively off-diagonal low-rank structure, which allows for fast
linear algebra computations. Suppressing the factor of data dimension, the
memory and arithmetic complexities for training a regression or a classifier
are reduced from $O(n^2)$ and $O(n^3)$ to $O(nr)$ and $O(nr^2)$, respectively,
where $n$ is the number of training examples and $r$ is the rank on each level
of the hierarchy. Although other randomized approximate kernels entail a
similar complexity, empirical results show that the proposed kernel achieves a
matching performance with a smaller $r$. We demonstrate comprehensive
experiments to show the effective use of the proposed kernel on data sizes up
to the order of millions.
| Jie Chen, Haim Avron, Vikas Sindhwani | null | 1608.0086 | null | null |
PageRank in Malware Categorization | cs.CR cs.LG | In this paper, we propose a malware categorization method that models malware
behavior in terms of instructions using PageRank. PageRank computes ranks of
web pages based on structural information and can also compute ranks of
instructions that represent the structural information of the instructions in
malware analysis methods. Our malware categorization method uses the computed
ranks as features in machine learning algorithms. In the evaluation, we compare
the effectiveness of different PageRank algorithms and also investigate bagging
and boosting algorithms to improve the categorization accuracy.
| BooJoong Kang, Suleiman Y. Yerima, Kieran McLaughlin, Sakir Sezer | 10.1145/2811411.2811514 | 1608.00866 | null | null |
Relational Similarity Machines | stat.ML cs.AI cs.LG | This paper proposes Relational Similarity Machines (RSM): a fast, accurate,
and flexible relational learning framework for supervised and semi-supervised
learning tasks. Despite the importance of relational learning, most existing
methods are hard to adapt to different settings, due to issues with efficiency,
scalability, accuracy, and flexibility for handling a wide variety of
classification problems, data, constraints, and tasks. For instance, many
existing methods perform poorly for multi-class classification problems, graphs
that are sparsely labeled or network data with low relational autocorrelation.
In contrast, the proposed relational learning framework is designed to be (i)
fast for learning and inference at real-time interactive rates, and (ii)
flexible for a variety of learning settings (multi-class problems), constraints
(few labeled instances), and application domains. The experiments demonstrate
the effectiveness of RSM for a variety of tasks and data.
| Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed | null | 1608.00876 | null | null |
RETURNN: The RWTH Extensible Training framework for Universal Recurrent
Neural Networks | cs.LG cs.CL cs.NE | In this work we release our extensible and easily configurable neural network
training software. It provides a rich set of functional layers with a
particular focus on efficient training of recurrent neural network topologies
on multiple GPUs. The source of the software package is public and freely
available for academic research purposes and can be used as a framework or as a
standalone tool which supports a flexible configuration. The software allows to
train state-of-the-art deep bidirectional long short-term memory (LSTM) models
on both one dimensional data like speech or two dimensional data like
handwritten text and was used to develop successful submission systems in
several evaluation campaigns.
| Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf
Schl\"uter, Hermann Ney | null | 1608.00895 | null | null |
Community Detection Algorithm Combining Stochastic Block Model and
Attribute Data Clustering | cs.SI cs.LG physics.soc-ph | We propose a new algorithm to detect the community structure in a network
that utilizes both the network structure and vertex attribute data. Suppose we
have the network structure together with the vertex attribute data, that is,
the information assigned to each vertex associated with the community to which
it belongs. The problem addressed this paper is the detection of the community
structure from the information of both the network structure and the vertex
attribute data. Our approach is based on the Bayesian approach that models the
posterior probability distribution of the community labels. The detection of
the community structure in our method is achieved by using belief propagation
and an EM algorithm. We numerically verified the performance of our method
using computer-generated networks and real-world networks.
| Shun Kataoka, Takuto Kobayashi, Muneki Yasuda, and Kazuyuki Tanaka | 10.7566/JPSJ.85.114802 | 1608.0092 | null | null |
Fuzzy c-Shape: A new algorithm for clustering finite time series
waveforms | cs.LG | The existence of large volumes of time series data in many applications has
motivated data miners to investigate specialized methods for mining time series
data. Clustering is a popular data mining method due to its powerful
exploratory nature and its usefulness as a preprocessing step for other data
mining techniques. This article develops two novel clustering algorithms for
time series data that are extensions of a crisp c-shapes algorithm. The two new
algorithms are heuristic derivatives of fuzzy c-means (FCM). Fuzzy c-Shapes
plus (FCS+) replaces the inner product norm in the FCM model with a shape-based
distance function. Fuzzy c-Shapes double plus (FCS++) uses the shape-based
distance, and also replaces the FCM cluster centers with shape-extracted
prototypes. Numerical experiments on 48 real time series data sets show that
the two new algorithms outperform state-of-the-art shape-based clustering
algorithms in terms of accuracy and efficiency. Four external cluster validity
indices (the Rand index, Adjusted Rand Index, Variation of Information, and
Normalized Mutual Information) are used to match candidate partitions generated
by each of the studied algorithms. All four indices agree that for these finite
waveform data sets, FCS++ gives a small improvement over FCS+, and in turn,
FCS+ is better than the original crisp c-shapes method. Finally, we apply two
tests of statistical significance to the three algorithms. The Wilcoxon and
Friedman statistics both rank the three algorithms in exactly the same way as
the four cluster validity indices.
| Fateme Fahiman, Jame C.Bezdek, Sarah M.Erfani, Christopher Leckie,
Marimuthu Palaniswami | null | 1608.01072 | null | null |
Autonomous Grounding of Visual Field Experience through Sensorimotor
Prediction | cs.RO cs.AI cs.CV cs.LG | In a developmental framework, autonomous robots need to explore the world and
learn how to interact with it. Without an a priori model of the system, this
opens the challenging problem of having robots master their interface with the
world: how to perceive their environment using their sensors, and how to act in
it using their motors. The sensorimotor approach of perception claims that a
naive agent can learn to master this interface by capturing regularities in the
way its actions transform its sensory inputs. In this paper, we apply such an
approach to the discovery and mastery of the visual field associated with a
visual sensor. A computational model is formalized and applied to a simulated
system to illustrate the approach.
| Alban Laflaqui\`ere | null | 1608.01127 | null | null |
Ensemble-driven support vector clustering: From ensemble learning to
automatic parameter estimation | cs.LG | Support vector clustering (SVC) is a versatile clustering technique that is
able to identify clusters of arbitrary shapes by exploiting the kernel trick.
However, one hurdle that restricts the application of SVC lies in its
sensitivity to the kernel parameter and the trade-off parameter. Although many
extensions of SVC have been developed, to the best of our knowledge, there is
still no algorithm that is able to effectively estimate the two crucial
parameters in SVC without supervision. In this paper, we propose a novel
support vector clustering approach termed ensemble-driven support vector
clustering (EDSVC), which for the first time tackles the automatic parameter
estimation problem for SVC based on ensemble learning, and is capable of
producing robust clustering results in a purely unsupervised manner.
Experimental results on multiple real-world datasets demonstrate the
effectiveness of our approach.
| Dong Huang, Chang-Dong Wang, Jian-Huang Lai, Yun Liang, Shan Bian, Yu
Chen | null | 1608.01198 | null | null |
Learning a Driving Simulator | cs.LG stat.ML | Comma.ai's approach to Artificial Intelligence for self-driving cars is based
on an agent that learns to clone driver behaviors and plans maneuvers by
simulating future events in the road. This paper illustrates one of our
research approaches for driving simulation. One where we learn to simulate.
Here we investigate variational autoencoders with classical and learned cost
functions using generative adversarial networks for embedding road frames.
Afterwards, we learn a transition model in the embedded space using action
conditioned Recurrent Neural Networks. We show that our approach can keep
predicting realistic looking video for several frames despite the transition
model being optimized without a cost function in the pixel space.
| Eder Santana, George Hotz | null | 1608.0123 | null | null |
Improving Quality of Hierarchical Clustering for Large Data Series | cs.CL cs.LG | Brown clustering is a hard, hierarchical, bottom-up clustering of words in a
vocabulary. Words are assigned to clusters based on their usage pattern in a
given corpus. The resulting clusters and hierarchical structure can be used in
constructing class-based language models and for generating features to be used
in NLP tasks. Because of its high computational cost, the most-used version of
Brown clustering is a greedy algorithm that uses a window to restrict its
search space. Like other clustering algorithms, Brown clustering finds a
sub-optimal, but nonetheless effective, mapping of words to clusters. Because
of its ability to produce high-quality, human-understandable cluster, Brown
clustering has seen high uptake the NLP research community where it is used in
the preprocessing and feature generation steps.
Little research has been done towards improving the quality of Brown
clusters, despite the greedy and heuristic nature of the algorithm. The
approaches tried so far have focused on: studying the effect of the
initialisation in a similar algorithm; tuning the parameters used to define the
desired number of clusters and the behaviour of the algorithm; and including a
separate parameter to differentiate the window from the desired number of
clusters. However, some of these approaches have not yielded significant
improvements in cluster quality.
In this thesis, a close analysis of the Brown algorithm is provided,
revealing important under-specifications and weaknesses in the original
algorithm. These have serious effects on cluster quality and reproducibility of
research using Brown clustering. In the second part of the thesis, two
modifications are proposed. Finally, a thorough evaluation is performed,
considering both the optimization criterion of Brown clustering and the
performance of the resulting class-based language models.
| Manuel R. Ciosici | null | 1608.01238 | null | null |
Fast and Simple Optimization for Poisson Likelihood Models | cs.LG math.OC stat.ML | Poisson likelihood models have been prevalently used in imaging, social
networks, and time series analysis. We propose fast, simple,
theoretically-grounded, and versatile, optimization algorithms for Poisson
likelihood modeling. The Poisson log-likelihood is concave but not
Lipschitz-continuous. Since almost all gradient-based optimization algorithms
rely on Lipschitz-continuity, optimizing Poisson likelihood models with a
guarantee of convergence can be challenging, especially for large-scale
problems.
We present a new perspective allowing to efficiently optimize a wide range of
penalized Poisson likelihood objectives. We show that an appropriate saddle
point reformulation enjoys a favorable geometry and a smooth structure.
Therefore, we can design a new gradient-based optimization algorithm with
$O(1/t)$ convergence rate, in contrast to the usual $O(1/\sqrt{t})$ rate of
non-smooth minimization alternatives. Furthermore, in order to tackle problems
with large samples, we also develop a randomized block-decomposition variant
that enjoys the same convergence rate yet more efficient iteration cost.
Experimental results on several point process applications including social
network estimation and temporal recommendation show that the proposed algorithm
and its randomized block variant outperform existing methods both on synthetic
and real-world datasets.
| Niao He, Zaid Harchaoui, Yichen Wang, Le Song | null | 1608.01264 | null | null |
Learning Online Alignments with Continuous Rewards Policy Gradient | cs.LG cs.CL | Sequence-to-sequence models with soft attention had significant success in
machine translation, speech recognition, and question answering. Though capable
and easy to use, they require that the entirety of the input sequence is
available at the beginning of inference, an assumption that is not valid for
instantaneous translation and speech recognition. To address this problem, we
present a new method for solving sequence-to-sequence problems using hard
online alignments instead of soft offline alignments. The online alignments
model is able to start producing outputs without the need to first process the
entire input sequence. A highly accurate online sequence-to-sequence model is
useful because it can be used to build an accurate voice-based instantaneous
translator. Our model uses hard binary stochastic decisions to select the
timesteps at which outputs will be produced. The model is trained to produce
these stochastic decisions using a standard policy gradient method. In our
experiments, we show that this model achieves encouraging performance on TIMIT
and Wall Street Journal (WSJ) speech recognition datasets.
| Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever | null | 1608.01281 | null | null |
Bayesian Kernel and Mutual $k$-Nearest Neighbor Regression | cs.LG stat.ML | We propose Bayesian extensions of two nonparametric regression methods which
are kernel and mutual $k$-nearest neighbor regression methods. Derived based on
Gaussian process models for regression, the extensions provide distributions
for target value estimates and the framework to select the hyperparameters. It
is shown that both the proposed methods asymptotically converge to kernel and
mutual $k$-nearest neighbor regression methods, respectively. The simulation
results show that the proposed methods can select proper hyperparameters and
are better than or comparable to the former methods for an artificial data set
and a real world data set.
| Hyun-Chul Kim | null | 1608.0141 | null | null |
A Distance for HMMs based on Aggregated Wasserstein Metric and State
Registration | cs.LG stat.ML | We propose a framework, named Aggregated Wasserstein, for computing a
dissimilarity measure or distance between two Hidden Markov Models with state
conditional distributions being Gaussian. For such HMMs, the marginal
distribution at any time spot follows a Gaussian mixture distribution, a fact
exploited to softly match, aka register, the states in two HMMs. We refer to
such HMMs as Gaussian mixture model-HMM (GMM-HMM). The registration of states
is inspired by the intrinsic relationship of optimal transport and the
Wasserstein metric between distributions. Specifically, the components of the
marginal GMMs are matched by solving an optimal transport problem where the
cost between components is the Wasserstein metric for Gaussian distributions.
The solution of the optimization problem is a fast approximation to the
Wasserstein metric between two GMMs. The new Aggregated Wasserstein distance is
a semi-metric and can be computed without generating Monte Carlo samples. It is
invariant to relabeling or permutation of the states. This distance quantifies
the dissimilarity of GMM-HMMs by measuring both the difference between the two
marginal GMMs and the difference between the two transition matrices. Our new
distance is tested on the tasks of retrieval and classification of time series.
Experiments on both synthetic data and real data have demonstrated its
advantages in terms of accuracy as well as efficiency in comparison with
existing distances based on the Kullback-Leibler divergence.
| Yukun Chen, Jianbo Ye, and Jia Li | null | 1608.01747 | null | null |
Forward Stagewise Additive Model for Collaborative Multiview Boosting | cs.LG | Multiview assisted learning has gained significant attention in recent years
in supervised learning genre. Availability of high performance computing
devices enables learning algorithms to search simultaneously over multiple
views or feature spaces to obtain an optimum classification performance. The
paper is a pioneering attempt of formulating a mathematical foundation for
realizing a multiview aided collaborative boosting architecture for multiclass
classification. Most of the present algorithms apply multiview learning
heuristically without exploring the fundamental mathematical changes imposed on
traditional boosting. Also, most of the algorithms are restricted to two class
or view setting. Our proposed mathematical framework enables collaborative
boosting across any finite dimensional view spaces for multiclass learning. The
boosting framework is based on forward stagewise additive model which minimizes
a novel exponential loss function. We show that the exponential loss function
essentially captures difficulty of a training sample space instead of the
traditional `1/0' loss. The new algorithm restricts a weak view from over
learning and thereby preventing overfitting. The model is inspired by our
earlier attempt on collaborative boosting which was devoid of mathematical
justification. The proposed algorithm is shown to converge much nearer to
global minimum in the exponential loss space and thus supersedes our previous
algorithm. The paper also presents analytical and numerical analysis of
convergence and margin bounds for multiview boosting algorithms and we show
that our proposed ensemble learning manifests lower error bound and higher
margin compared to our previous model. Also, the proposed model is compared
with traditional boosting and recent multiview boosting algorithms.
| Avisek Lahiri, Biswajit Paria, Prabir Kumar Biswas | null | 1608.01874 | null | null |
Kernel Ridge Regression via Partitioning | stat.ML cs.LG | In this paper, we investigate a divide and conquer approach to Kernel Ridge
Regression (KRR). Given n samples, the division step involves separating the
points based on some underlying disjoint partition of the input space (possibly
via clustering), and then computing a KRR estimate for each partition. The
conquering step is simple: for each partition, we only consider its own local
estimate for prediction. We establish conditions under which we can give
generalization bounds for this estimator, as well as achieve optimal minimax
rates. We also show that the approximation error component of the
generalization error is lesser than when a single KRR estimate is fit on the
data: thus providing both statistical and computational advantages over a
single KRR estimate over the entire data (or an averaging over random
partitions as in other recent work, [30]). Lastly, we provide experimental
validation for our proposed estimator and our assumptions.
| Rashish Tandon, Si Si, Pradeep Ravikumar, Inderjit Dhillon | null | 1608.01976 | null | null |
Communication-Efficient Parallel Block Minimization for Kernel Machines | cs.LG | Kernel machines often yield superior predictive performance on various tasks;
however, they suffer from severe computational challenges. In this paper, we
show how to overcome the important challenge of speeding up kernel machines. In
particular, we develop a parallel block minimization framework for solving
kernel machines, including kernel SVM and kernel logistic regression. Our
framework proceeds by dividing the problem into smaller subproblems by forming
a block-diagonal approximation of the Hessian matrix. The subproblems are then
solved approximately in parallel. After that, a communication efficient line
search procedure is developed to ensure sufficient reduction of the objective
function value at each iteration. We prove global linear convergence rate of
the proposed method with a wide class of subproblem solvers, and our analysis
covers strongly convex and some non-strongly convex functions. We apply our
algorithm to solve large-scale kernel SVM problems on distributed systems, and
show a significant improvement over existing parallel solvers. As an example,
on the covtype dataset with half-a-million samples, our algorithm can obtain an
approximate solution with 96% accuracy in 20 seconds using 32 machines, while
all the other parallel kernel SVM solvers require more than 2000 seconds to
achieve a solution with 95% accuracy. Moreover, our algorithm can scale to very
large data sets, such as the kdd algebra dataset with 8 million samples and 20
million features.
| Cho-Jui Hsieh and Si Si and Inderjit S. Dhillon | null | 1608.0201 | null | null |
Transferring Knowledge from Text to Predict Disease Onset | cs.LG cs.CL | In many domains such as medicine, training data is in short supply. In such
cases, external knowledge is often helpful in building predictive models. We
propose a novel method to incorporate publicly available domain expertise to
build accurate models. Specifically, we use word2vec models trained on a
domain-specific corpus to estimate the relevance of each feature's text
description to the prediction problem. We use these relevance estimates to
rescale the features, causing more important features to experience weaker
regularization.
We apply our method to predict the onset of five chronic diseases in the next
five years in two genders and two age groups. Our rescaling approach improves
the accuracy of the model, particularly when there are few positive examples.
Furthermore, our method selects 60% fewer features, easing interpretation by
physicians. Our method is applicable to other domains where feature and outcome
descriptions are available.
| Yun Liu, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su, Collin M. Stultz,
John V. Guttag | null | 1608.02071 | null | null |
Bi-directional Attention with Agreement for Dependency Parsing | cs.CL cs.AI cs.LG | We develop a novel bi-directional attention model for dependency parsing,
which learns to agree on headword predictions from the forward and backward
parsing directions. The parsing procedure for each direction is formulated as
sequentially querying the memory component that stores continuous headword
embeddings. The proposed parser makes use of {\it soft} headword embeddings,
allowing the model to implicitly capture high-order parsing history without
dramatically increasing the computational complexity. We conduct experiments on
English, Chinese, and 12 other languages from the CoNLL 2006 shared task,
showing that the proposed model achieves state-of-the-art unlabeled attachment
scores on 6 languages.
| Hao Cheng and Hao Fang and Xiaodong He and Jianfeng Gao and Li Deng | null | 1608.02076 | null | null |
How Much Did it Rain? Predicting Real Rainfall Totals Based on Radar
Data | cs.LG | We applied a variety of parametric and non-parametric machine learning models
to predict the probability distribution of rainfall based on 1M training
examples over a single year across several U.S. states. Our top performing
model based on a squared loss objective was a cross-validated parametric
k-nearest-neighbor predictor that took about six days to compute, and was
competitive in a world-wide competition.
| Adam Lesnikowski | null | 1608.02126 | null | null |
Spoofing 2D Face Detection: Machines See People Who Aren't There | cs.CR cs.CV cs.LG | Machine learning is increasingly used to make sense of the physical world yet
may suffer from adversarial manipulation. We examine the Viola-Jones 2D face
detection algorithm to study whether images can be created that humans do not
notice as faces yet the algorithm detects as faces. We show that it is possible
to construct images that Viola-Jones recognizes as containing faces yet no
human would consider a face. Moreover, we show that it is possible to construct
images that fool facial detection even when they are printed and then
photographed.
| Michael McCoyd and David Wagner | null | 1608.02128 | null | null |
Leveraging Union of Subspace Structure to Improve Constrained Clustering | cs.LG cs.CV | Many clustering problems in computer vision and other contexts are also
classification problems, where each cluster shares a meaningful label. Subspace
clustering algorithms in particular are often applied to problems that fit this
description, for example with face images or handwritten digits. While it is
straightforward to request human input on these datasets, our goal is to reduce
this input as much as possible. We present a pairwise-constrained clustering
algorithm that actively selects queries based on the union-of-subspaces model.
The central step of the algorithm is in querying points of minimum margin
between estimated subspaces; analogous to classifier margin, these lie near the
decision boundary. We prove that points lying near the intersection of
subspaces are points with low margin. Our procedure can be used after any
subspace clustering algorithm that outputs an affinity matrix. We demonstrate
on several datasets that our algorithm drives the clustering error down
considerably faster than the state-of-the-art active query algorithms on
datasets with subspace structure and is competitive on other datasets.
| John Lipor and Laura Balzano | null | 1608.02146 | null | null |
A General Characterization of the Statistical Query Complexity | cs.LG cs.CC stat.ML | Statistical query (SQ) algorithms are algorithms that have access to an {\em
SQ oracle} for the input distribution $D$ instead of i.i.d.~ samples from $D$.
Given a query function $\phi:X \rightarrow [-1,1]$, the oracle returns an
estimate of ${\bf E}_{ x\sim D}[\phi(x)]$ within some tolerance $\tau_\phi$
that roughly corresponds to the number of samples.
In this work we demonstrate that the complexity of solving general problems
over distributions using SQ algorithms can be captured by a relatively simple
notion of statistical dimension that we introduce. SQ algorithms capture a
broad spectrum of algorithmic approaches used in theory and practice, most
notably, convex optimization techniques. Hence our statistical dimension allows
to investigate the power of a variety of algorithmic approaches by analyzing a
single linear-algebraic parameter. Such characterizations were investigated
over the past 20 years in learning theory but prior characterizations are
restricted to the much simpler setting of classification problems relative to a
fixed distribution on the domain (Blum et al., 1994; Bshouty and Feldman, 2002;
Yang, 2001; Simon, 2007; Feldman, 2012; Szorenyi, 2009). Our characterization
is also the first to precisely characterize the necessary tolerance of queries.
We give applications of our techniques to two open problems in learning theory
and to algorithms that are subject to memory and communication constraints.
| Vitaly Feldman | null | 1608.02198 | null | null |
Deep Learning a Grasp Function for Grasping under Gripper Pose
Uncertainty | cs.RO cs.CV cs.LG | This paper presents a new method for parallel-jaw grasping of isolated
objects from depth images, under large gripper pose uncertainty. Whilst most
approaches aim to predict the single best grasp pose from an image, our method
first predicts a score for every possible grasp pose, which we denote the grasp
function. With this, it is possible to achieve grasping robust to the gripper's
pose uncertainty, by smoothing the grasp function with the pose uncertainty
function. Therefore, if the single best pose is adjacent to a region of poor
grasp quality, that pose will no longer be chosen, and instead a pose will be
chosen which is surrounded by a region of high grasp quality. To learn this
function, we train a Convolutional Neural Network which takes as input a single
depth image of an object, and outputs a score for each grasp pose across the
image. Training data for this is generated by use of physics simulation and
depth image simulation with 3D object meshes, to enable acquisition of
sufficient data without requiring exhaustive real-world experiments. We
evaluate with both synthetic and real experiments, and show that the learned
grasp score is more robust to gripper pose uncertainty than when this
uncertainty is not accounted for.
| Edward Johns, Stefan Leutenegger and Andrew J. Davison | null | 1608.02239 | null | null |
Robust High-Dimensional Linear Regression | cs.LG cs.CR stat.ML | The effectiveness of supervised learning techniques has made them ubiquitous
in research and practice. In high-dimensional settings, supervised learning
commonly relies on dimensionality reduction to improve performance and identify
the most important factors in predicting outcomes. However, the economic
importance of learning has made it a natural target for adversarial
manipulation of training data, which we term poisoning attacks. Prior
approaches to dealing with robust supervised learning rely on strong
assumptions about the nature of the feature matrix, such as feature
independence and sub-Gaussian noise with low variance. We propose an integrated
method for robust regression that relaxes these assumptions, assuming only that
the feature matrix can be well approximated by a low-rank matrix. Our
techniques integrate improved robust low-rank matrix approximation and robust
principle component regression, and yield strong performance guarantees.
Moreover, we experimentally show that our methods significantly outperform
state of the art both in running time and prediction error.
| Chang Liu, Bo Li, Yevgeniy Vorobeychik, Alina Oprea | null | 1608.02257 | null | null |
Online Adaptation of Deep Architectures with Reinforcement Learning | cs.LG cs.NE | Online learning has become crucial to many problems in machine learning. As
more data is collected sequentially, quickly adapting to changes in the data
distribution can offer several competitive advantages such as avoiding loss of
prior knowledge and more efficient learning. However, adaptation to changes in
the data distribution (also known as covariate shift) needs to be performed
without compromising past knowledge already built in into the model to cope
with voluminous and dynamic data. In this paper, we propose an online stacked
Denoising Autoencoder whose structure is adapted through reinforcement
learning. Our algorithm forces the network to exploit and explore favourable
architectures employing an estimated utility function that maximises the
accuracy of an unseen validation sequence. Different actions, such as Pool,
Increment and Merge are available to modify the structure of the network. As we
observe through a series of experiments, our approach is more responsive,
robust, and principled than its counterparts for non-stationary as well as
stationary data distributions. Experimental results indicate that our algorithm
performs better at preserving gained prior knowledge and responding to changes
in the data distribution.
| Thushan Ganegedara, Lionel Ott and Fabio Ramos | null | 1608.02292 | null | null |
Uncovering Voice Misuse Using Symbolic Mismatch | cs.LG | Voice disorders affect an estimated 14 million working-aged Americans, and
many more worldwide. We present the first large scale study of vocal misuse
based on long-term ambulatory data collected by an accelerometer placed on the
neck. We investigate an unsupervised data mining approach to uncovering latent
information about voice misuse.
We segment signals from over 253 days of data from 22 subjects into over a
hundred million single glottal pulses (closures of the vocal folds), cluster
segments into symbols, and use symbolic mismatch to uncover differences between
patients and matched controls, and between patients pre- and post-treatment.
Our results show significant behavioral differences between patients and
controls, as well as between some pre- and post-treatment patients. Our
proposed approach provides an objective basis for helping diagnose behavioral
voice disorders, and is a first step towards a more data-driven understanding
of the impact of voice therapy.
| Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta, Jarrad H. Van Stan,
Robert E. Hillman, and John V. Guttag | null | 1608.02301 | null | null |
Towards Representation Learning with Tractable Probabilistic Models | cs.LG cs.AI stat.ML | Probabilistic models learned as density estimators can be exploited in
representation learning beside being toolboxes used to answer inference queries
only. However, how to extract useful representations highly depends on the
particular model involved. We argue that tractable inference, i.e. inference
that can be computed in polynomial time, can enable general schemes to extract
features from black box models. We plan to investigate how Tractable
Probabilistic Models (TPMs) can be exploited to generate embeddings by random
query evaluations. We devise two experimental designs to assess and compare
different TPMs as feature extractors in an unsupervised representation learning
framework. We show some experimental results on standard image datasets by
applying such a method to Sum-Product Networks and Mixture of Trees as
tractable models generating embeddings.
| Antonio Vergari and Nicola Di Mauro and Floriana Esposito | null | 1608.02341 | null | null |
Interpolated Discretized Embedding of Single Vectors and Vector Pairs
for Classification, Metric Learning and Distance Approximation | cs.LG | We propose a new embedding method for a single vector and for a pair of
vectors. This embedding method enables: a) efficient classification and
regression of functions of single vectors; b) efficient approximation of
distance functions; and c) non-Euclidean, semimetric learning. To the best of
our knowledge, this is the first work that enables learning any general,
non-Euclidean, semimetrics. That is, our method is a universal semimetric
learning and approximation method that can approximate any distance function
with as high accuracy as needed with or without semimetric constraints. The
project homepage including code is at: http://www.ariel.ac.il/sites/ofirpele/ID
| Ofir Pele and Yakir Ben-Aliz | null | 1608.02484 | null | null |
Multi-task Domain Adaptation for Sequence Tagging | cs.CL cs.LG | Many domain adaptation approaches rely on learning cross domain shared
representations to transfer the knowledge learned in one domain to other
domains. Traditional domain adaptation only considers adapting for one task. In
this paper, we explore multi-task representation learning under the domain
adaptation scenario. We propose a neural network framework that supports domain
adaptation for multiple tasks simultaneously, and learns shared representations
that better generalize for domain adaptation. We apply the proposed framework
to domain adaptation for sequence tagging problems considering two tasks:
Chinese word segmentation and named entity recognition. Experiments show that
multi-task domain adaptation works better than disjoint domain adaptation for
each task, and achieves the state-of-the-art results for both tasks in the
social media domain.
| Nanyun Peng and Mark Dredze | null | 1608.02689 | null | null |
Deeply Semantic Inductive Spatio-Temporal Learning | cs.AI cs.CV cs.LG cs.LO | We present an inductive spatio-temporal learning framework rooted in
inductive logic programming. With an emphasis on visuo-spatial language, logic,
and cognition, the framework supports learning with relational spatio-temporal
features identifiable in a range of domains involving the processing and
interpretation of dynamic visuo-spatial imagery. We present a prototypical
system, and an example application in the domain of computing for visual arts
and computational cognitive science.
| Jakob Suchan and Mehul Bhatt and Carl Schultz | null | 1608.02693 | null | null |
Mean Box Pooling: A Rich Image Representation and Output Embedding for
the Visual Madlibs Task | cs.CV cs.AI cs.CL cs.LG | We present Mean Box Pooling, a novel visual representation that pools over
CNN representations of a large number, highly overlapping object proposals. We
show that such representation together with nCCA, a successful multimodal
embedding technique, achieves state-of-the-art performance on the Visual
Madlibs task. Moreover, inspired by the nCCA's objective function, we extend
classical CNN+LSTM approach to train the network by directly maximizing the
similarity between the internal representation of the deep learning
architecture and candidate answers. Again, such approach achieves a significant
improvement over the prior work that also uses CNN+LSTM approach on Visual
Madlibs.
| Ashkan Mokarian and Mateusz Malinowski and Mario Fritz | null | 1608.02717 | null | null |
OnionNet: Sharing Features in Cascaded Deep Classifiers | cs.CV cs.LG cs.NE | The focus of our work is speeding up evaluation of deep neural networks in
retrieval scenarios, where conventional architectures may spend too much time
on negative examples. We propose to replace a monolithic network with our novel
cascade of feature-sharing deep classifiers, called OnionNet, where subsequent
stages may add both new layers as well as new feature channels to the previous
ones. Importantly, intermediate feature maps are shared among classifiers,
preventing them from the necessity of being recomputed. To accomplish this, the
model is trained end-to-end in a principled way under a joint loss. We validate
our approach in theory and on a synthetic benchmark. As a result demonstrated
in three applications (patch matching, object detection, and image retrieval),
our cascade can operate significantly faster than both monolithic networks and
traditional cascades without sharing at the cost of marginal decrease in
precision.
| Martin Simonovsky and Nikos Komodakis | null | 1608.02728 | null | null |
Posterior Sampling for Reinforcement Learning Without Episodes | stat.ML cs.LG | This is a brief technical note to clarify some of the issues with applying
the application of the algorithm posterior sampling for reinforcement learning
(PSRL) in environments without fixed episodes. In particular, this paper aims
to:
- Review some of results which have been proven for finite horizon MDPs
(Osband et al 2013, 2014a, 2014b, 2016) and also for MDPs with finite ergodic
structure (Gopalan et al 2014).
- Review similar results for optimistic algorithms in infinite horizon
problems (Jaksch et al 2010, Bartlett and Tewari 2009, Abbasi-Yadkori and
Szepesvari 2011), with particular attention to the dynamic episode growth.
- Highlight the delicate technical issue which has led to a fault in the
proof of the lazy-PSRL algorithm (Abbasi-Yadkori and Szepesvari 2015). We
present an explicit counterexample to this style of argument. Therefore, we
suggest that the Theorem 2 in (Abbasi-Yadkori and Szepesvari 2015) be instead
considered a conjecture, as it has no rigorous proof.
- Present pragmatic approaches to apply PSRL in infinite horizon problems. We
conjecture that, under some additional assumptions, it will be possible to
obtain bounds $O( \sqrt{T} )$ even without episodic reset.
We hope that this note serves to clarify existing results in the field of
reinforcement learning and provides interesting motivation for future work.
| Ian Osband, Benjamin Van Roy | null | 1608.02731 | null | null |
On Lower Bounds for Regret in Reinforcement Learning | stat.ML cs.LG | This is a brief technical note to clarify the state of lower bounds on regret
for reinforcement learning. In particular, this paper:
- Reproduces a lower bound on regret for reinforcement learning, similar to
the result of Theorem 5 in the journal UCRL2 paper (Jaksch et al 2010).
- Clarifies that the proposed proof of Theorem 6 in the REGAL paper (Bartlett
and Tewari 2009) does not hold using the standard techniques without further
work. We suggest that this result should instead be considered a conjecture as
it has no rigorous proof.
- Suggests that the conjectured lower bound given by (Bartlett and Tewari
2009) is incorrect and, in fact, it is possible to improve the scaling of the
upper bound to match the weaker lower bounds presented in this paper.
We hope that this note serves to clarify existing results in the field of
reinforcement learning and provides interesting motivation for future work.
| Ian Osband, Benjamin Van Roy | null | 1608.02732 | null | null |
Classification with the pot-pot plot | stat.ML cs.LG | We propose a procedure for supervised classification that is based on
potential functions. The potential of a class is defined as a kernel density
estimate multiplied by the class's prior probability. The method transforms the
data to a potential-potential (pot-pot) plot, where each data point is mapped
to a vector of potentials. Separation of the classes, as well as classification
of new data points, is performed on this plot. For this, either the
$\alpha$-procedure ($\alpha$-P) or $k$-nearest neighbors ($k$-NN) are employed.
For data that are generated from continuous distributions, these classifiers
prove to be strongly Bayes-consistent. The potentials depend on the kernel and
its bandwidth used in the density estimate. We investigate several variants of
bandwidth selection, including joint and separate pre-scaling and a bandwidth
regression approach. The new method is applied to benchmark data from the
literature, including simulated data sets as well as 50 sets of real data. It
compares favorably to known classification methods such as LDA, QDA, max kernel
density estimates, $k$-NN, and $DD$-plot classification using depth functions.
| Oleksii Pokotylo and Karl Mosler | null | 1608.02861 | null | null |
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