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Preserving Differential Privacy Between Features in Distributed
Estimation | stat.ML cs.CR cs.DC cs.LG | Privacy is crucial in many applications of machine learning. Legal, ethical
and societal issues restrict the sharing of sensitive data making it difficult
to learn from datasets that are partitioned between many parties. One important
instance of such a distributed setting arises when information about each
record in the dataset is held by different data owners (the design matrix is
"vertically-partitioned").
In this setting few approaches exist for private data sharing for the
purposes of statistical estimation and the classical setup of differential
privacy with a "trusted curator" preparing the data does not apply. We work
with the notion of $(\epsilon,\delta)$-distributed differential privacy which
extends single-party differential privacy to the distributed,
vertically-partitioned case. We propose PriDE, a scalable framework for
distributed estimation where each party communicates perturbed random
projections of their locally held features ensuring
$(\epsilon,\delta)$-distributed differential privacy is preserved. For
$\ell_2$-penalized supervised learning problems PriDE has bounded estimation
error compared with the optimal estimates obtained without privacy constraints
in the non-distributed setting. We confirm this empirically on real world and
synthetic datasets.
| Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen | 10.1002/sta4.189 | 1703.00403 | null | null |
Detecting Adversarial Samples from Artifacts | stat.ML cs.LG | Deep neural networks (DNNs) are powerful nonlinear architectures that are
known to be robust to random perturbations of the input. However, these models
are vulnerable to adversarial perturbations--small input changes crafted
explicitly to fool the model. In this paper, we ask whether a DNN can
distinguish adversarial samples from their normal and noisy counterparts. We
investigate model confidence on adversarial samples by looking at Bayesian
uncertainty estimates, available in dropout neural networks, and by performing
density estimation in the subspace of deep features learned by the model. The
result is a method for implicit adversarial detection that is oblivious to the
attack algorithm. We evaluate this method on a variety of standard datasets
including MNIST and CIFAR-10 and show that it generalizes well across different
architectures and attacks. Our findings report that 85-93% ROC-AUC can be
achieved on a number of standard classification tasks with a negative class
that consists of both normal and noisy samples.
| Reuben Feinman, Ryan R. Curtin, Saurabh Shintre, Andrew B. Gardner | null | 1703.0041 | null | null |
Virtual-to-real Deep Reinforcement Learning: Continuous Control of
Mobile Robots for Mapless Navigation | cs.RO cs.AI cs.LG | We present a learning-based mapless motion planner by taking the sparse
10-dimensional range findings and the target position with respect to the
mobile robot coordinate frame as input and the continuous steering commands as
output. Traditional motion planners for mobile ground robots with a laser range
sensor mostly depend on the obstacle map of the navigation environment where
both the highly precise laser sensor and the obstacle map building work of the
environment are indispensable. We show that, through an asynchronous deep
reinforcement learning method, a mapless motion planner can be trained
end-to-end without any manually designed features and prior demonstrations. The
trained planner can be directly applied in unseen virtual and real
environments. The experiments show that the proposed mapless motion planner can
navigate the nonholonomic mobile robot to the desired targets without colliding
with any obstacles.
| Lei Tai, Giuseppe Paolo and Ming Liu | null | 1703.0042 | null | null |
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for
Regularized Empirical Risk Minimization | math.OC cs.LG stat.ML | In this paper, we develop a new accelerated stochastic gradient method for
efficiently solving the convex regularized empirical risk minimization problem
in mini-batch settings. The use of mini-batches is becoming a golden standard
in the machine learning community, because mini-batch settings stabilize the
gradient estimate and can easily make good use of parallel computing. The core
of our proposed method is the incorporation of our new "double acceleration"
technique and variance reduction technique. We theoretically analyze our
proposed method and show that our method much improves the mini-batch
efficiencies of previous accelerated stochastic methods, and essentially only
needs size $\sqrt{n}$ mini-batches for achieving the optimal iteration
complexities for both non-strongly and strongly convex objectives, where $n$ is
the training set size. Further, we show that even in non-mini-batch settings,
our method achieves the best known convergence rate for both non-strongly and
strongly convex objectives.
| Tomoya Murata and Taiji Suzuki | null | 1703.00439 | null | null |
Fast k-Nearest Neighbour Search via Prioritized DCI | cs.LG cs.AI cs.DS cs.IR stat.ML | Most exact methods for k-nearest neighbour search suffer from the curse of
dimensionality; that is, their query times exhibit exponential dependence on
either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing
(DCI) offers a promising way of circumventing the curse and successfully
reduces the dependence of query time on intrinsic dimensionality from
exponential to sublinear. In this paper, we propose a variant of DCI, which we
call Prioritized DCI, and show a remarkable improvement in the dependence of
query time on intrinsic dimensionality. In particular, a linear increase in
intrinsic dimensionality, or equivalently, an exponential increase in the
number of points near a query, can be mostly counteracted with just a linear
increase in space. We also demonstrate empirically that Prioritized DCI
significantly outperforms prior methods. In particular, relative to
Locality-Sensitive Hashing (LSH), Prioritized DCI reduces the number of
distance evaluations by a factor of 14 to 116 and the memory consumption by a
factor of 21.
| Ke Li and Jitendra Malik | null | 1703.0044 | null | null |
Learning to Optimize Neural Nets | cs.LG cs.AI math.OC stat.ML | Learning to Optimize is a recently proposed framework for learning
optimization algorithms using reinforcement learning. In this paper, we explore
learning an optimization algorithm for training shallow neural nets. Such
high-dimensional stochastic optimization problems present interesting
challenges for existing reinforcement learning algorithms. We develop an
extension that is suited to learning optimization algorithms in this setting
and demonstrate that the learned optimization algorithm consistently
outperforms other known optimization algorithms even on unseen tasks and is
robust to changes in stochasticity of gradients and the neural net
architecture. More specifically, we show that an optimization algorithm trained
with the proposed method on the problem of training a neural net on MNIST
generalizes to the problems of training neural nets on the Toronto Faces
Dataset, CIFAR-10 and CIFAR-100.
| Ke Li and Jitendra Malik | null | 1703.00441 | null | null |
OptNet: Differentiable Optimization as a Layer in Neural Networks | cs.LG cs.AI math.OC stat.ML | This paper presents OptNet, a network architecture that integrates
optimization problems (here, specifically in the form of quadratic programs) as
individual layers in larger end-to-end trainable deep networks. These layers
encode constraints and complex dependencies between the hidden states that
traditional convolutional and fully-connected layers often cannot capture. We
explore the foundations for such an architecture: we show how techniques from
sensitivity analysis, bilevel optimization, and implicit differentiation can be
used to exactly differentiate through these layers and with respect to layer
parameters; we develop a highly efficient solver for these layers that exploits
fast GPU-based batch solves within a primal-dual interior point method, and
which provides backpropagation gradients with virtually no additional cost on
top of the solve; and we highlight the application of these approaches in
several problems. In one notable example, the method is learns to play
mini-Sudoku (4x4) given just input and output games, with no a-priori
information about the rules of the game; this highlights the ability of OptNet
to learn hard constraints better than other neural architectures.
| Brandon Amos, J. Zico Kolter | null | 1703.00443 | null | null |
Reinforcement Learning for Pivoting Task | cs.RO cs.LG | In this work we propose an approach to learn a robust policy for solving the
pivoting task. Recently, several model-free continuous control algorithms were
shown to learn successful policies without prior knowledge of the dynamics of
the task. However, obtaining successful policies required thousands to millions
of training episodes, limiting the applicability of these approaches to real
hardware. We developed a training procedure that allows us to use a simple
custom simulator to learn policies robust to the mismatch of simulation vs
robot. In our experiments, we demonstrate that the policy learned in the
simulator is able to pivot the object to the desired target angle on the real
robot. We also show generalization to an object with different inertia, shape,
mass and friction properties than those used during training. This result is a
step towards making model-free reinforcement learning available for solving
robotics tasks via pre-training in simulators that offer only an imprecise
match to the real-world dynamics.
| Rika Antonova, Silvia Cruciani, Christian Smith, Danica Kragic | null | 1703.00472 | null | null |
Truth and Regret in Online Scheduling | cs.GT cs.AI cs.DS cs.LG | We consider a scheduling problem where a cloud service provider has multiple
units of a resource available over time. Selfish clients submit jobs, each with
an arrival time, deadline, length, and value. The service provider's goal is to
implement a truthful online mechanism for scheduling jobs so as to maximize the
social welfare of the schedule. Recent work shows that under a stochastic
assumption on job arrivals, there is a single-parameter family of mechanisms
that achieves near-optimal social welfare. We show that given any such family
of near-optimal online mechanisms, there exists an online mechanism that in the
worst case performs nearly as well as the best of the given mechanisms. Our
mechanism is truthful whenever the mechanisms in the given family are truthful
and prompt, and achieves optimal (within constant factors) regret.
We model the problem of competing against a family of online scheduling
mechanisms as one of learning from expert advice. A primary challenge is that
any scheduling decisions we make affect not only the payoff at the current
step, but also the resource availability and payoffs in future steps.
Furthermore, switching from one algorithm (a.k.a. expert) to another in an
online fashion is challenging both because it requires synchronization with the
state of the latter algorithm as well as because it affects the incentive
structure of the algorithms. We further show how to adapt our algorithm to a
non-clairvoyant setting where job lengths are unknown until jobs are run to
completion. Once again, in this setting, we obtain truthfulness along with
asymptotically optimal regret (within poly-logarithmic factors).
| Shuchi Chawla, Nikhil Devanur, Janardhan Kulkarni, Rad Niazadeh | null | 1703.00484 | null | null |
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and
Comparison | cs.LG cs.AI | The selection, development, or comparison of machine learning methods in data
mining can be a difficult task based on the target problem and goals of a
particular study. Numerous publicly available real-world and simulated
benchmark datasets have emerged from different sources, but their organization
and adoption as standards have been inconsistent. As such, selecting and
curating specific benchmarks remains an unnecessary burden on machine learning
practitioners and data scientists. The present study introduces an accessible,
curated, and developing public benchmark resource to facilitate identification
of the strengths and weaknesses of different machine learning methodologies. We
compare meta-features among the current set of benchmark datasets in this
resource to characterize the diversity of available data. Finally, we apply a
number of established machine learning methods to the entire benchmark suite
and analyze how datasets and algorithms cluster in terms of performance. This
work is an important first step towards understanding the limitations of
popular benchmarking suites and developing a resource that connects existing
benchmarking standards to more diverse and efficient standards in the future.
| Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J.
Urbanowicz, Jason H. Moore | null | 1703.00512 | null | null |
Understanding Synthetic Gradients and Decoupled Neural Interfaces | cs.LG cs.NE | When training neural networks, the use of Synthetic Gradients (SG) allows
layers or modules to be trained without update locking - without waiting for a
true error gradient to be backpropagated - resulting in Decoupled Neural
Interfaces (DNIs). This unlocked ability of being able to update parts of a
neural network asynchronously and with only local information was demonstrated
to work empirically in Jaderberg et al (2016). However, there has been very
little demonstration of what changes DNIs and SGs impose from a functional,
representational, and learning dynamics point of view. In this paper, we study
DNIs through the use of synthetic gradients on feed-forward networks to better
understand their behaviour and elucidate their effect on optimisation. We show
that the incorporation of SGs does not affect the representational strength of
the learning system for a neural network, and prove the convergence of the
learning system for linear and deep linear models. On practical problems we
investigate the mechanism by which synthetic gradient estimators approximate
the true loss, and, surprisingly, how that leads to drastically different
layer-wise representations. Finally, we also expose the relationship of using
synthetic gradients to other error approximation techniques and find a unifying
language for discussion and comparison.
| Wojciech Marian Czarnecki, Grzegorz \'Swirszcz, Max Jaderberg, Simon
Osindero, Oriol Vinyals, Koray Kavukcuoglu | null | 1703.00522 | null | null |
Human Interaction with Recommendation Systems | stat.ML cs.LG | Many recommendation algorithms rely on user data to generate recommendations.
However, these recommendations also affect the data obtained from future users.
This work aims to understand the effects of this dynamic interaction. We
propose a simple model where users with heterogeneous preferences arrive over
time. Based on this model, we prove that naive estimators, i.e. those which
ignore this feedback loop, are not consistent. We show that consistent
estimators are efficient in the presence of myopic agents. Our results are
validated using extensive simulations.
| Sven Schmit and Carlos Riquelme | null | 1703.00535 | null | null |
Model-Independent Online Learning for Influence Maximization | cs.LG | We consider influence maximization (IM) in social networks, which is the
problem of maximizing the number of users that become aware of a product by
selecting a set of "seed" users to expose the product to. While prior work
assumes a known model of information diffusion, we propose a novel
parametrization that not only makes our framework agnostic to the underlying
diffusion model, but also statistically efficient to learn from data. We give a
corresponding monotone, submodular surrogate function, and show that it is a
good approximation to the original IM objective. We also consider the case of a
new marketer looking to exploit an existing social network, while
simultaneously learning the factors governing information propagation. For
this, we propose a pairwise-influence semi-bandit feedback model and develop a
LinUCB-based bandit algorithm. Our model-independent analysis shows that our
regret bound has a better (as compared to previous work) dependence on the size
of the network. Experimental evaluation suggests that our framework is robust
to the underlying diffusion model and can efficiently learn a near-optimal
solution.
| Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh,
Laks Lakshmanan, Mark Schmidt | null | 1703.00557 | null | null |
An Analytical Formula of Population Gradient for two-layered ReLU
network and its Applications in Convergence and Critical Point Analysis | cs.LG | In this paper, we explore theoretical properties of training a two-layered
ReLU network $g(\mathbf{x}; \mathbf{w}) = \sum_{j=1}^K
\sigma(\mathbf{w}_j^T\mathbf{x})$ with centered $d$-dimensional spherical
Gaussian input $\mathbf{x}$ ($\sigma$=ReLU). We train our network with gradient
descent on $\mathbf{w}$ to mimic the output of a teacher network with the same
architecture and fixed parameters $\mathbf{w}^*$. We show that its population
gradient has an analytical formula, leading to interesting theoretical analysis
of critical points and convergence behaviors. First, we prove that critical
points outside the hyperplane spanned by the teacher parameters
("out-of-plane") are not isolated and form manifolds, and characterize in-plane
critical-point-free regions for two ReLU case. On the other hand, convergence
to $\mathbf{w}^*$ for one ReLU node is guaranteed with at least
$(1-\epsilon)/2$ probability, if weights are initialized randomly with standard
deviation upper-bounded by $O(\epsilon/\sqrt{d})$, consistent with empirical
practice. For network with many ReLU nodes, we prove that an infinitesimal
perturbation of weight initialization results in convergence towards
$\mathbf{w}^*$ (or its permutation), a phenomenon known as spontaneous
symmetric-breaking (SSB) in physics. We assume no independence of ReLU
activations. Simulation verifies our findings.
| Yuandong Tian | null | 1703.0056 | null | null |
Signal-based Bayesian Seismic Monitoring | cs.LG physics.geo-ph | Detecting weak seismic events from noisy sensors is a difficult perceptual
task. We formulate this task as Bayesian inference and propose a generative
model of seismic events and signals across a network of spatially distributed
stations. Our system, SIGVISA, is the first to directly model seismic
waveforms, allowing it to incorporate a rich representation of the physics
underlying the signal generation process. We use Gaussian processes over
wavelet parameters to predict detailed waveform fluctuations based on
historical events, while degrading smoothly to simple parametric envelopes in
regions with no historical seismicity. Evaluating on data from the western US,
we recover three times as many events as previous work, and reduce mean
location errors by a factor of four while greatly increasing sensitivity to
low-magnitude events.
| David A. Moore and Stuart J. Russell | null | 1703.00561 | null | null |
MoleculeNet: A Benchmark for Molecular Machine Learning | cs.LG physics.chem-ph stat.ML | Molecular machine learning has been maturing rapidly over the last few years.
Improved methods and the presence of larger datasets have enabled machine
learning algorithms to make increasingly accurate predictions about molecular
properties. However, algorithmic progress has been limited due to the lack of a
standard benchmark to compare the efficacy of proposed methods; most new
algorithms are benchmarked on different datasets making it challenging to gauge
the quality of proposed methods. This work introduces MoleculeNet, a large
scale benchmark for molecular machine learning. MoleculeNet curates multiple
public datasets, establishes metrics for evaluation, and offers high quality
open-source implementations of multiple previously proposed molecular
featurization and learning algorithms (released as part of the DeepChem open
source library). MoleculeNet benchmarks demonstrate that learnable
representations are powerful tools for molecular machine learning and broadly
offer the best performance. However, this result comes with caveats. Learnable
representations still struggle to deal with complex tasks under data scarcity
and highly imbalanced classification. For quantum mechanical and biophysical
datasets, the use of physics-aware featurizations can be more important than
choice of particular learning algorithm.
| Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb
Geniesse, Aneesh S. Pappu, Karl Leswing and Vijay Pande | null | 1703.00564 | null | null |
Generalization and Equilibrium in Generative Adversarial Nets (GANs) | cs.LG cs.NE stat.ML | We show that training of generative adversarial network (GAN) may not have
good generalization properties; e.g., training may appear successful but the
trained distribution may be far from target distribution in standard metrics.
However, generalization does occur for a weaker metric called neural net
distance. It is also shown that an approximate pure equilibrium exists in the
discriminator/generator game for a special class of generators with natural
training objectives when generator capacity and training set sizes are
moderate.
This existence of equilibrium inspires MIX+GAN protocol, which can be
combined with any existing GAN training, and empirically shown to improve some
of them.
| Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang | null | 1703.00573 | null | null |
Active Learning for Accurate Estimation of Linear Models | stat.ML cs.LG | We explore the sequential decision making problem where the goal is to
estimate uniformly well a number of linear models, given a shared budget of
random contexts independently sampled from a known distribution. The decision
maker must query one of the linear models for each incoming context, and
receives an observation corrupted by noise levels that are unknown, and depend
on the model instance. We present Trace-UCB, an adaptive allocation algorithm
that learns the noise levels while balancing contexts accordingly across the
different linear functions, and derive guarantees for simple regret in both
expectation and high-probability. Finally, we extend the algorithm and its
guarantees to high dimensional settings, where the number of linear models
times the dimension of the contextual space is higher than the total budget of
samples. Simulations with real data suggest that Trace-UCB is remarkably
robust, outperforming a number of baselines even when its assumptions are
violated.
| Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric | null | 1703.00579 | null | null |
Positive-Unlabeled Learning with Non-Negative Risk Estimator | cs.LG stat.ML | From only positive (P) and unlabeled (U) data, a binary classifier could be
trained with PU learning, in which the state of the art is unbiased PU
learning. However, if its model is very flexible, empirical risks on training
data will go negative, and we will suffer from serious overfitting. In this
paper, we propose a non-negative risk estimator for PU learning: when getting
minimized, it is more robust against overfitting, and thus we are able to use
very flexible models (such as deep neural networks) given limited P data.
Moreover, we analyze the bias, consistency, and mean-squared-error reduction of
the proposed risk estimator, and bound the estimation error of the resulting
empirical risk minimizer. Experiments demonstrate that our risk estimator fixes
the overfitting problem of its unbiased counterparts.
| Ryuichi Kiryo, Gang Niu, Marthinus C. du Plessis, and Masashi Sugiyama | null | 1703.00593 | null | null |
In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential
Importance Sampling | cs.LG cs.DB stat.ML | Entity resolution (ER) presents unique challenges for evaluation methodology.
While crowdsourcing platforms acquire ground truth, sound approaches to
sampling must drive labelling efforts. In ER, extreme class imbalance between
matching and non-matching records can lead to enormous labelling requirements
when seeking statistically consistent estimates for rigorous evaluation. This
paper addresses this important challenge with the OASIS algorithm: a sampler
and F-measure estimator for ER evaluation. OASIS draws samples from a (biased)
instrumental distribution, chosen to ensure estimators with optimal asymptotic
variance. As new labels are collected OASIS updates this instrumental
distribution via a Bayesian latent variable model of the annotator oracle, to
quickly focus on unlabelled items providing more information. We prove that
resulting estimates of F-measure, precision, recall converge to the true
population values. Thorough comparisons of sampling methods on a variety of ER
datasets demonstrate significant labelling reductions of up to 83% without loss
to estimate accuracy.
| Neil G. Marchant and Benjamin I. P. Rubinstein | null | 1703.00617 | null | null |
Traffic-Aware Transmission Mode Selection in D2D-enabled Cellular
Networks with Token System | cs.IT cs.LG math.IT | We consider a D2D-enabled cellular network where user equipments (UEs) owned
by rational users are incentivized to form D2D pairs using tokens. They
exchange tokens electronically to "buy" and "sell" D2D services. Meanwhile the
devices have the ability to choose the transmission mode, i.e. receiving data
via cellular links or D2D links. Thus taking the different benefits brought by
diverse traffic types as a prior, the UEs can utilize their tokens more
efficiently via transmission mode selection. In this paper, the optimal
transmission mode selection strategy as well as token collection policy are
investigated to maximize the long-term utility in the dynamic network
environment. The optimal policy is proved to be a threshold strategy, and the
thresholds have a monotonicity property. Numerical simulations verify our
observations and the gain from transmission mode selection is observed.
| Yiling Yuan, Tao Yang, Hui Feng, Bo Hu, Jianqiu Zhang, Bin Wang and
Qiyong Lu | null | 1703.0066 | null | null |
Introduction to Nonnegative Matrix Factorization | cs.NA cs.CV cs.LG math.OC stat.ML | In this paper, we introduce and provide a short overview of nonnegative
matrix factorization (NMF). Several aspects of NMF are discussed, namely, the
application in hyperspectral imaging, geometry and uniqueness of NMF solutions,
complexity, algorithms, and its link with extended formulations of polyhedra.
In order to put NMF into perspective, the more general problem class of
constrained low-rank matrix approximation problems is first briefly introduced.
| Nicolas Gillis | null | 1703.00663 | null | null |
Adaptive Matching for Expert Systems with Uncertain Task Types | cs.AI cs.LG stat.ML | A matching in a two-sided market often incurs an externality: a matched
resource may become unavailable to the other side of the market, at least for a
while. This is especially an issue in online platforms involving human experts
as the expert resources are often scarce. The efficient utilization of experts
in these platforms is made challenging by the fact that the information
available about the parties involved is usually limited.
To address this challenge, we develop a model of a task-expert matching
system where a task is matched to an expert using not only the prior
information about the task but also the feedback obtained from the past
matches. In our model the tasks arrive online while the experts are fixed and
constrained by a finite service capacity. For this model, we characterize the
maximum task resolution throughput a platform can achieve. We show that the
natural greedy approaches where each expert is assigned a task most suitable to
her skill is suboptimal, as it does not internalize the above externality. We
develop a throughput optimal backpressure algorithm which does so by accounting
for the `congestion' among different task types. Finally, we validate our model
and confirm our theoretical findings with data-driven simulations via logs of
Math.StackExchange, a StackOverflow forum dedicated to mathematics.
| Virag Shah, Lennart Gulikers, Laurent Massoulie, Milan Vojnovic | null | 1703.00674 | null | null |
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels | cs.LG stat.ML | Non-linear kernel methods can be approximated by fast linear ones using
suitable explicit feature maps allowing their application to large scale
problems. We investigate how convolution kernels for structured data are
composed from base kernels and construct corresponding feature maps. On this
basis we propose exact and approximative feature maps for widely used graph
kernels based on the kernel trick. We analyze for which kernels and graph
properties computation by explicit feature maps is feasible and actually more
efficient. In particular, we derive approximative, explicit feature maps for
state-of-the-art kernels supporting real-valued attributes including the
GraphHopper and graph invariant kernels. In extensive experiments we show that
our approaches often achieve a classification accuracy close to the exact
methods based on the kernel trick, but require only a fraction of their running
time. Moreover, we propose and analyze algorithms for computing random walk,
shortest-path and subgraph matching kernels by explicit and implicit feature
maps. Our theoretical results are confirmed experimentally by observing a phase
transition when comparing running time with respect to label diversity, walk
lengths and subgraph size, respectively.
| Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting,
Petra Mutzel | 10.1007/s10618-019-00652-0 | 1703.00676 | null | null |
Mixing Complexity and its Applications to Neural Networks | cs.LG | We suggest analyzing neural networks through the prism of space constraints.
We observe that most training algorithms applied in practice use bounded
memory, which enables us to use a new notion introduced in the study of
space-time tradeoffs that we call mixing complexity. This notion was devised in
order to measure the (in)ability to learn using a bounded-memory algorithm. In
this paper we describe how we use mixing complexity to obtain new results on
what can and cannot be learned using neural networks.
| Michal Moshkovitz, Naftali Tishby | null | 1703.00729 | null | null |
Distributed Bayesian Matrix Factorization with Limited Communication | stat.ML cs.DC cs.LG cs.NA stat.ME | Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank
representations of matrices and for predicting missing values and providing
confidence intervals. Scaling up the posterior inference for massive-scale
matrices is challenging and requires distributing both data and computation
over many workers, making communication the main computational bottleneck.
Embarrassingly parallel inference would remove the communication needed, by
using completely independent computations on different data subsets, but it
suffers from the inherent unidentifiability of BMF solutions. We introduce a
hierarchical decomposition of the joint posterior distribution, which couples
the subset inferences, allowing for embarrassingly parallel computations in a
sequence of at most three stages. Using an efficient approximate
implementation, we show improvements empirically on both real and simulated
data. Our distributed approach is able to achieve a speed-up of almost an order
of magnitude over the full posterior, with a negligible effect on predictive
accuracy. Our method outperforms state-of-the-art embarrassingly parallel MCMC
methods in accuracy, and achieves results competitive to other available
distributed and parallel implementations of BMF.
| Xiangju Qin, Paul Blomstedt, Eemeli Lepp\"aaho, Pekka Parviainen,
Samuel Kaski | 10.1007/s10994-019-05778-2 | 1703.00734 | null | null |
Wireless Interference Identification with Convolutional Neural Networks | cs.LG cs.CV | The steadily growing use of license-free frequency bands requires reliable
coexistence management for deterministic medium utilization. For interference
mitigation, proper wireless interference identification (WII) is essential. In
this work we propose the first WII approach based upon deep convolutional
neural networks (CNNs). The CNN naively learns its features through
self-optimization during an extensive data-driven GPU-based training process.
We propose a CNN example which is based upon sensing snapshots with a limited
duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs
between 15 classes. They represent packet transmissions of IEEE 802.11 b/g,
IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the
2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII
approaches and has a classification accuracy greater than 95% for
signal-to-noise ratio of at least -5 dB.
| Malte Schmidt, Dimitri Block, Uwe Meier | 10.1109/indin.2017.8104767 | 1703.00737 | null | null |
Predicting Rankings of Software Verification Competitions | cs.LG cs.SE | Software verification competitions, such as the annual SV-COMP, evaluate
software verification tools with respect to their effectivity and efficiency.
Typically, the outcome of a competition is a (possibly category-specific)
ranking of the tools. For many applications, such as building portfolio
solvers, it would be desirable to have an idea of the (relative) performance of
verification tools on a given verification task beforehand, i.e., prior to
actually running all tools on the task.
In this paper, we present a machine learning approach to predicting rankings
of tools on verification tasks. The method builds upon so-called label ranking
algorithms, which we complement with appropriate kernels providing a similarity
measure for verification tasks. Our kernels employ a graph representation for
software source code that mixes elements of control flow and program dependence
graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate
our rank prediction technique to generalize well and achieve a rather high
predictive accuracy. In particular, our method outperforms a recently proposed
feature-based approach of Demyanova et al. (when applied to rank predictions).
| Mike Czech, Eyke H\"ullermeier, Marie-Christine Jakobs, Heike Wehrheim | null | 1703.00757 | null | null |
Attentive Recurrent Comparators | cs.CV cs.LG | Rapid learning requires flexible representations to quickly adopt to new
evidence. We develop a novel class of models called Attentive Recurrent
Comparators (ARCs) that form representations of objects by cycling through them
and making observations. Using the representations extracted by ARCs, we
develop a way of approximating a \textit{dynamic representation space} and use
it for one-shot learning. In the task of one-shot classification on the
Omniglot dataset, we achieve the state of the art performance with an error
rate of 1.5\%. This represents the first super-human result achieved for this
task with a generic model that uses only pixel information.
| Pranav Shyam and Shubham Gupta and Ambedkar Dukkipati | null | 1703.00767 | null | null |
A Generic Online Parallel Learning Framework for Large Margin Models | cs.CL cs.LG | To speed up the training process, many existing systems use parallel
technology for online learning algorithms. However, most research mainly focus
on stochastic gradient descent (SGD) instead of other algorithms. We propose a
generic online parallel learning framework for large margin models, and also
analyze our framework on popular large margin algorithms, including MIRA and
Structured Perceptron. Our framework is lock-free and easy to implement on
existing systems. Experiments show that systems with our framework can gain
near linear speed up by increasing running threads, and with no loss in
accuracy.
| Shuming Ma and Xu Sun | null | 1703.00786 | null | null |
A Robust Adaptive Stochastic Gradient Method for Deep Learning | cs.LG | Stochastic gradient algorithms are the main focus of large-scale optimization
problems and led to important successes in the recent advancement of the deep
learning algorithms. The convergence of SGD depends on the careful choice of
learning rate and the amount of the noise in stochastic estimates of the
gradients. In this paper, we propose an adaptive learning rate algorithm, which
utilizes stochastic curvature information of the loss function for
automatically tuning the learning rates. The information about the element-wise
curvature of the loss function is estimated from the local statistics of the
stochastic first order gradients. We further propose a new variance reduction
technique to speed up the convergence. In our experiments with deep neural
networks, we obtained better performance compared to the popular stochastic
gradient algorithms.
| Caglar Gulcehre, Jose Sotelo, Marcin Moczulski, Yoshua Bengio | null | 1703.00788 | null | null |
Unsupervised Steganalysis Based on Artificial Training Sets | cs.MM cs.LG | In this paper, an unsupervised steganalysis method that combines artificial
training setsand supervised classification is proposed. We provide a formal
framework for unsupervisedclassification of stego and cover images in the
typical situation of targeted steganalysis (i.e.,for a known algorithm and
approximate embedding bit rate). We also present a completeset of experiments
using 1) eight different image databases, 2) image features based on
RichModels, and 3) three different embedding algorithms: Least Significant Bit
(LSB) matching,Highly undetectable steganography (HUGO) and Wavelet Obtained
Weights (WOW). Weshow that the experimental results outperform previous methods
based on Rich Models inthe majority of the tested cases. At the same time, the
proposed approach bypasses theproblem of Cover Source Mismatch -when the
embedding algorithm and bit rate are known-, since it removes the need of a
training database when we have a large enough testing set.Furthermore, we
provide a generic proof of the proposed framework in the machine
learningcontext. Hence, the results of this paper could be extended to other
classification problemssimilar to steganalysis.
| Daniel Lerch-Hostalot and David Meg\'ias | 10.1016/j.engappai.2015.12.013 | 1703.00796 | null | null |
Opening the Black Box of Deep Neural Networks via Information | cs.LG | Despite their great success, there is still no comprehensive theoretical
understanding of learning with Deep Neural Networks (DNNs) or their inner
organization. Previous work proposed to analyze DNNs in the \textit{Information
Plane}; i.e., the plane of the Mutual Information values that each layer
preserves on the input and output variables. They suggested that the goal of
the network is to optimize the Information Bottleneck (IB) tradeoff between
compression and prediction, successively, for each layer.
In this work we follow up on this idea and demonstrate the effectiveness of
the Information-Plane visualization of DNNs. Our main results are: (i) most of
the training epochs in standard DL are spent on {\emph compression} of the
input to efficient representation and not on fitting the training labels. (ii)
The representation compression phase begins when the training errors becomes
small and the Stochastic Gradient Decent (SGD) epochs change from a fast drift
to smaller training error into a stochastic relaxation, or random diffusion,
constrained by the training error value. (iii) The converged layers lie on or
very close to the Information Bottleneck (IB) theoretical bound, and the maps
from the input to any hidden layer and from this hidden layer to the output
satisfy the IB self-consistent equations. This generalization through noise
mechanism is unique to Deep Neural Networks and absent in one layer networks.
(iv) The training time is dramatically reduced when adding more hidden layers.
Thus the main advantage of the hidden layers is computational. This can be
explained by the reduced relaxation time, as this it scales super-linearly
(exponentially for simple diffusion) with the information compression from the
previous layer.
| Ravid Shwartz-Ziv and Naftali Tishby | null | 1703.0081 | null | null |
Robust Communication-Optimal Distributed Clustering Algorithms | cs.DS cs.DC cs.LG | In this work, we study the $k$-median and $k$-means clustering problems when
the data is distributed across many servers and can contain outliers. While
there has been a lot of work on these problems for worst-case instances, we
focus on gaining a finer understanding through the lens of beyond worst-case
analysis. Our main motivation is the following: for many applications such as
clustering proteins by function or clustering communities in a social network,
there is some unknown target clustering, and the hope is that running a
$k$-median or $k$-means algorithm will produce clusterings which are close to
matching the target clustering. Worst-case results can guarantee constant
factor approximations to the optimal $k$-median or $k$-means objective value,
but not closeness to the target clustering.
Our first result is a distributed algorithm which returns a near-optimal
clustering assuming a natural notion of stability, namely, approximation
stability [Balcan et. al 2013], even when a constant fraction of the data are
outliers. The communication complexity is $\tilde O(sk+z)$ where $s$ is the
number of machines, $k$ is the number of clusters, and $z$ is the number of
outliers.
Next, we show this amount of communication cannot be improved even in the
setting when the input satisfies various non-worst-case assumptions. We give a
matching $\Omega(sk+z)$ lower bound on the communication required both for
approximating the optimal $k$-means or $k$-median cost up to any constant, and
for returning a clustering that is close to the target clustering in Hamming
distance. These lower bounds hold even when the data satisfies approximation
stability or other common notions of stability, and the cluster sizes are
balanced. Therefore, $\Omega(sk+z)$ is a communication bottleneck, even for
real-world instances.
| Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, and
David Woodruff | null | 1703.0083 | null | null |
Meta Networks | cs.LG stat.ML | Neural networks have been successfully applied in applications with a large
amount of labeled data. However, the task of rapid generalization on new
concepts with small training data while preserving performances on previously
learned ones still presents a significant challenge to neural network models.
In this work, we introduce a novel meta learning method, Meta Networks
(MetaNet), that learns a meta-level knowledge across tasks and shifts its
inductive biases via fast parameterization for rapid generalization. When
evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve
a near human-level performance and outperform the baseline approaches by up to
6% accuracy. We demonstrate several appealing properties of MetaNet relating to
generalization and continual learning.
| Tsendsuren Munkhdalai and Hong Yu | null | 1703.00837 | null | null |
Encrypted accelerated least squares regression | stat.ML cs.LG | Information that is stored in an encrypted format is, by definition, usually
not amenable to statistical analysis or machine learning methods. In this paper
we present detailed analysis of coordinate and accelerated gradient descent
algorithms which are capable of fitting least squares and penalised ridge
regression models, using data encrypted under a fully homomorphic encryption
scheme. Gradient descent is shown to dominate in terms of encrypted
computational speed, and theoretical results are proven to give parameter
bounds which ensure correctness of decryption. The characteristics of encrypted
computation are empirically shown to favour a non-standard acceleration
technique. This demonstrates the possibility of approximating conventional
statistical regression methods using encrypted data without compromising
privacy.
| Pedro M. Esperan\c{c}a, Louis J. M. Aslett, Chris C. Holmes | null | 1703.00839 | null | null |
Exact Topology Reconstruction of Radial Dynamical Systems with
Applications to Distribution System of the Power Grid | cs.LG cs.SY | In this article we present a method to reconstruct the interconnectedness of
dynamically related stochastic processes, where the interactions are
bi-directional and the underlying topology is a tree. Our approach is based on
multivariate Wiener filtering which recovers spurious edges apart from the true
edges in the topology reconstruction. The main contribution of this work is to
show that all spurious links obtained using Wiener filtering can be eliminated
if the underlying topology is a tree based on which we present a three stage
network reconstruction procedure for trees. We illustrate the effectiveness of
the method developed by applying it on a typical distribution system of the
electric grid.
| Saurav Talukdar, Deepjyoti Deka, Donatello Materassi and Murti V.
Salapaka | null | 1703.00847 | null | null |
Learning the Structure of Generative Models without Labeled Data | cs.LG stat.ML | Curating labeled training data has become the primary bottleneck in machine
learning. Recent frameworks address this bottleneck with generative models to
synthesize labels at scale from weak supervision sources. The generative
model's dependency structure directly affects the quality of the estimated
labels, but selecting a structure automatically without any labeled data is a
distinct challenge. We propose a structure estimation method that maximizes the
$\ell_1$-regularized marginal pseudolikelihood of the observed data. Our
analysis shows that the amount of unlabeled data required to identify the true
structure scales sublinearly in the number of possible dependencies for a broad
class of models. Simulations show that our method is 100$\times$ faster than a
maximum likelihood approach and selects $1/4$ as many extraneous dependencies.
We also show that our method provides an average of 1.5 F1 points of
improvement over existing, user-developed information extraction applications
on real-world data such as PubMed journal abstracts.
| Stephen H. Bach, Bryan He, Alexander Ratner, Christopher R\'e | null | 1703.00854 | null | null |
Binarized Convolutional Landmark Localizers for Human Pose Estimation
and Face Alignment with Limited Resources | cs.CV cs.LG stat.ML | Our goal is to design architectures that retain the groundbreaking
performance of CNNs for landmark localization and at the same time are
lightweight, compact and suitable for applications with limited computational
resources. To this end, we make the following contributions: (a) we are the
first to study the effect of neural network binarization on localization tasks,
namely human pose estimation and face alignment. We exhaustively evaluate
various design choices, identify performance bottlenecks, and more importantly
propose multiple orthogonal ways to boost performance. (b) Based on our
analysis, we propose a novel hierarchical, parallel and multi-scale residual
architecture that yields large performance improvement over the standard
bottleneck block while having the same number of parameters, thus bridging the
gap between the original network and its binarized counterpart. (c) We perform
a large number of ablation studies that shed light on the properties and the
performance of the proposed block. (d) We present results for experiments on
the most challenging datasets for human pose estimation and face alignment,
reporting in many cases state-of-the-art performance. Code can be downloaded
from https://www.adrianbulat.com/binary-cnn-landmarks
| Adrian Bulat and Georgios Tzimiropoulos | 10.1109/ICCV.2017.400 | 1703.00862 | null | null |
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning | cs.LG cs.CV stat.ML | We draw a formal connection between using synthetic training data to optimize
neural network parameters and approximate, Bayesian, model-based reasoning. In
particular, training a neural network using synthetic data can be viewed as
learning a proposal distribution generator for approximate inference in the
synthetic-data generative model. We demonstrate this connection in a
recognition task where we develop a novel Captcha-breaking architecture and
train it using synthetic data, demonstrating both state-of-the-art performance
and a way of computing task-specific posterior uncertainty. Using a neural
network trained this way, we also demonstrate successful breaking of real-world
Captchas currently used by Facebook and Wikipedia. Reasoning from these
empirical results and drawing connections with Bayesian modeling, we discuss
the robustness of synthetic data results and suggest important considerations
for ensuring good neural network generalization when training with synthetic
data.
| Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank Wood | null | 1703.00868 | null | null |
How to Escape Saddle Points Efficiently | cs.LG math.OC stat.ML | This paper shows that a perturbed form of gradient descent converges to a
second-order stationary point in a number iterations which depends only
poly-logarithmically on dimension (i.e., it is almost "dimension-free"). The
convergence rate of this procedure matches the well-known convergence rate of
gradient descent to first-order stationary points, up to log factors. When all
saddle points are non-degenerate, all second-order stationary points are local
minima, and our result thus shows that perturbed gradient descent can escape
saddle points almost for free. Our results can be directly applied to many
machine learning applications, including deep learning. As a particular
concrete example of such an application, we show that our results can be used
directly to establish sharp global convergence rates for matrix factorization.
Our results rely on a novel characterization of the geometry around saddle
points, which may be of independent interest to the non-convex optimization
community.
| Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I.
Jordan | null | 1703.00887 | null | null |
Being Robust (in High Dimensions) Can Be Practical | cs.LG cs.DS cs.IT math.IT stat.ML | Robust estimation is much more challenging in high dimensions than it is in
one dimension: Most techniques either lead to intractable optimization problems
or estimators that can tolerate only a tiny fraction of errors. Recent work in
theoretical computer science has shown that, in appropriate distributional
models, it is possible to robustly estimate the mean and covariance with
polynomial time algorithms that can tolerate a constant fraction of
corruptions, independent of the dimension. However, the sample and time
complexity of these algorithms is prohibitively large for high-dimensional
applications. In this work, we address both of these issues by establishing
sample complexity bounds that are optimal, up to logarithmic factors, as well
as giving various refinements that allow the algorithms to tolerate a much
larger fraction of corruptions. Finally, we show on both synthetic and real
data that our algorithms have state-of-the-art performance and suddenly make
high-dimensional robust estimation a realistic possibility.
| Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur
Moitra, Alistair Stewart | null | 1703.00893 | null | null |
Toward Controlled Generation of Text | cs.LG cs.AI cs.CL stat.ML | Generic generation and manipulation of text is challenging and has limited
success compared to recent deep generative modeling in visual domain. This
paper aims at generating plausible natural language sentences, whose attributes
are dynamically controlled by learning disentangled latent representations with
designated semantics. We propose a new neural generative model which combines
variational auto-encoders and holistic attribute discriminators for effective
imposition of semantic structures. With differentiable approximation to
discrete text samples, explicit constraints on independent attribute controls,
and efficient collaborative learning of generator and discriminators, our model
learns highly interpretable representations from even only word annotations,
and produces realistic sentences with desired attributes. Quantitative
evaluation validates the accuracy of sentence and attribute generation.
| Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P.
Xing | null | 1703.00955 | null | null |
A Laplacian Framework for Option Discovery in Reinforcement Learning | cs.LG cs.AI | Representation learning and option discovery are two of the biggest
challenges in reinforcement learning (RL). Proto-value functions (PVFs) are a
well-known approach for representation learning in MDPs. In this paper we
address the option discovery problem by showing how PVFs implicitly define
options. We do it by introducing eigenpurposes, intrinsic reward functions
derived from the learned representations. The options discovered from
eigenpurposes traverse the principal directions of the state space. They are
useful for multiple tasks because they are discovered without taking the
environment's rewards into consideration. Moreover, different options act at
different time scales, making them helpful for exploration. We demonstrate
features of eigenpurposes in traditional tabular domains as well as in Atari
2600 games.
| Marlos C. Machado and Marc G. Bellemare and Michael Bowling | null | 1703.00956 | null | null |
Self-Paced Multitask Learning with Shared Knowledge | stat.ML cs.LG | This paper introduces self-paced task selection to multitask learning, where
instances from more closely related tasks are selected in a progression of
easier-to-harder tasks, to emulate an effective human education strategy, but
applied to multitask machine learning. We develop the mathematical foundation
for the approach based on iterative selection of the most appropriate task,
learning the task parameters, and updating the shared knowledge, optimizing a
new bi-convex loss function. This proposed method applies quite generally,
including to multitask feature learning, multitask learning with alternating
structure optimization, etc. Results show that in each of the above
formulations self-paced (easier-to-harder) task selection outperforms the
baseline version of these methods in all the experiments.
| Keerthiram Murugesan, Jaime Carbonell | null | 1703.00977 | null | null |
Compositional Falsification of Cyber-Physical Systems with Machine
Learning Components | cs.SY cs.LG cs.SE | Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks.
| Tommaso Dreossi, Alexandre Donz\'e, Sanjit A. Seshia | null | 1703.00978 | null | null |
Belief Propagation in Conditional RBMs for Structured Prediction | cs.LG cs.CV stat.ML | Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular
models for a wide range of applications. In previous work, learning on such
models has been dominated by contrastive divergence~(CD) and its variants.
Belief propagation~(BP) algorithms are believed to be slow for structured
prediction on conditional RBMs~(e.g., Mnih et al. [2011]), and not as good as
CD when applied in learning~(e.g., Larochelle et al. [2012]). In this work, we
present a matrix-based implementation of belief propagation algorithms on
CRBMs, which is easily scalable to tens of thousands of visible and hidden
units. We demonstrate that, in both maximum likelihood and max-margin learning,
training conditional RBMs with BP as the inference routine can provide
significantly better results than current state-of-the-art CD methods on
structured prediction problems. We also include practical guidelines on
training CRBMs with BP, and some insights on the interaction of learning and
inference algorithms for CRBMs.
| Wei Ping, Alexander Ihler | null | 1703.00986 | null | null |
Optimization of distributions differences for classification | cs.LG stat.ML | In this paper we introduce a new classification algorithm called Optimization
of Distributions Differences (ODD). The algorithm aims to find a transformation
from the feature space to a new space where the instances in the same class are
as close as possible to one another while the gravity centers of these classes
are as far as possible from one another. This aim is formulated as a
multiobjective optimization problem that is solved by a hybrid of an
evolutionary strategy and the Quasi-Newton method. The choice of the
transformation function is flexible and could be any continuous space function.
We experiment with a linear and a non-linear transformation in this paper. We
show that the algorithm can outperform 6 other state-of-the-art classification
methods, namely naive Bayes, support vector machines, linear discriminant
analysis, multi-layer perceptrons, decision trees, and k-nearest neighbors, in
12 standard classification datasets. Our results show that the method is less
sensitive to the imbalanced number of instances comparing to these methods. We
also show that ODD maintains its performance better than other classification
methods in these datasets, hence, offers a better generalization ability.
| Mohammad Reza Bonyadi, Quang M. Tieng, David C. Reutens | null | 1703.00989 | null | null |
Co-Clustering for Multitask Learning | stat.ML cs.LG | This paper presents a new multitask learning framework that learns a shared
representation among the tasks, incorporating both task and feature clusters.
The jointly-induced clusters yield a shared latent subspace where task
relationships are learned more effectively and more generally than in
state-of-the-art multitask learning methods. The proposed general framework
enables the derivation of more specific or restricted state-of-the-art
multitask methods. The paper also proposes a highly-scalable multitask learning
algorithm, based on the new framework, using conjugate gradient descent and
generalized \textit{Sylvester equations}. Experimental results on synthetic and
benchmark datasets show that the proposed method systematically outperforms
several state-of-the-art multitask learning methods.
| Keerthiram Murugesan, Jaime Carbonell, Yiming Yang | null | 1703.00994 | null | null |
Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion
Prediction | cs.LG | Tracking congestion throughout the network road is a critical component of
Intelligent transportation network management systems. Understanding how the
traffic flows and short-term prediction of congestion occurrence due to
rush-hour or incidents can be beneficial to such systems to effectively manage
and direct the traffic to the most appropriate detours. Many of the current
traffic flow prediction systems are designed by utilizing a central processing
component where the prediction is carried out through aggregation of the
information gathered from all measuring stations. However, centralized systems
are not scalable and fail provide real-time feedback to the system whereas in a
decentralized scheme, each node is responsible to predict its own short-term
congestion based on the local current measurements in neighboring nodes.
We propose a decentralized deep learning-based method where each node
accurately predicts its own congestion state in real-time based on the
congestion state of the neighboring stations. Moreover, historical data from
the deployment site is not required, which makes the proposed method more
suitable for newly installed stations. In order to achieve higher performance,
we introduce a regularized Euclidean loss function that favors high congestion
samples over low congestion samples to avoid the impact of the unbalanced
training dataset. A novel dataset for this purpose is designed based on the
traffic data obtained from traffic control stations in northern California.
Extensive experiments conducted on the designed benchmark reflect a successful
congestion prediction.
| Mohammadhani Fouladgar, Mostafa Parchami, Ramez Elmasri, Amir Ghaderi | 10.1109/IJCNN.2017.7966128 | 1703.01006 | null | null |
Active Learning for Cost-Sensitive Classification | cs.LG stat.ML | We design an active learning algorithm for cost-sensitive multiclass
classification: problems where different errors have different costs. Our
algorithm, COAL, makes predictions by regressing to each label's cost and
predicting the smallest. On a new example, it uses a set of regressors that
perform well on past data to estimate possible costs for each label. It queries
only the labels that could be the best, ignoring the sure losers. We prove COAL
can be efficiently implemented for any regression family that admits squared
loss optimization; it also enjoys strong guarantees with respect to predictive
performance and labeling effort. We empirically compare COAL to passive
learning and several active learning baselines, showing significant
improvements in labeling effort and test cost on real-world datasets.
| Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daume III,
John Langford | null | 1703.01014 | null | null |
Unsupervised Basis Function Adaptation for Reinforcement Learning | cs.AI cs.LG stat.ML | When using reinforcement learning (RL) algorithms to evaluate a policy it is
common, given a large state space, to introduce some form of approximation
architecture for the value function (VF). The exact form of this architecture
can have a significant effect on the accuracy of the VF estimate, however, and
determining a suitable approximation architecture can often be a highly complex
task. Consequently there is a large amount of interest in the potential for
allowing RL algorithms to adaptively generate approximation architectures.
We investigate a method of adapting approximation architectures which uses
feedback regarding the frequency with which an agent has visited certain states
to guide which areas of the state space to approximate with greater detail.
This method is "unsupervised" in the sense that it makes no direct reference to
reward or the VF estimate. We introduce an algorithm based upon this idea which
adapts a state aggregation approximation architecture on-line.
A common method of scoring a VF estimate is to weight the squared Bellman
error of each state-action by the probability of that state-action occurring.
Adopting this scoring method, and assuming $S$ states, we demonstrate
theoretically that - provided (1) the number of cells $X$ in the state
aggregation architecture is of order $\sqrt{S}\log_2{S}\ln{S}$ or greater, (2)
the policy and transition function are close to deterministic, and (3) the
prior for the transition function is uniformly distributed - our algorithm,
used in conjunction with a suitable RL algorithm, can guarantee a score which
is arbitrarily close to zero as $S$ becomes large. It is able to do this
despite having only $O(X \log_2S)$ space complexity and negligible time
complexity. The results take advantage of certain properties of the stationary
distributions of Markov chains.
| Edward W. Barker and Charl J. Ras | null | 1703.01026 | null | null |
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential
Prediction | cs.LG | Researchers have demonstrated state-of-the-art performance in sequential
decision making problems (e.g., robotics control, sequential prediction) with
deep neural network models. One often has access to near-optimal oracles that
achieve good performance on the task during training. We demonstrate that
AggreVaTeD --- a policy gradient extension of the Imitation Learning (IL)
approach of (Ross & Bagnell, 2014) --- can leverage such an oracle to achieve
faster and better solutions with less training data than a less-informed
Reinforcement Learning (RL) technique. Using both feedforward and recurrent
neural network predictors, we present stochastic gradient procedures on a
sequential prediction task, dependency-parsing from raw image data, as well as
on various high dimensional robotics control problems. We also provide a
comprehensive theoretical study of IL that demonstrates we can expect up to
exponentially lower sample complexity for learning with AggreVaTeD than with RL
algorithms, which backs our empirical findings. Our results and theory indicate
that the proposed approach can achieve superior performance with respect to the
oracle when the demonstrator is sub-optimal.
| Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew
Bagnell | null | 1703.0103 | null | null |
Learning Robot Activities from First-Person Human Videos Using
Convolutional Future Regression | cs.RO cs.AI cs.CV cs.LG | We design a new approach that allows robot learning of new activities from
unlabeled human example videos. Given videos of humans executing the same
activity from a human's viewpoint (i.e., first-person videos), our objective is
to make the robot learn the temporal structure of the activity as its future
regression network, and learn to transfer such model for its own motor
execution. We present a new deep learning model: We extend the state-of-the-art
convolutional object detection network for the representation/estimation of
human hands in training videos, and newly introduce the concept of using a
fully convolutional network to regress (i.e., predict) the intermediate scene
representation corresponding to the future frame (e.g., 1-2 seconds later).
Combining these allows direct prediction of future locations of human hands and
objects, which enables the robot to infer the motor control plan using our
manipulation network. We experimentally confirm that our approach makes
learning of robot activities from unlabeled human interaction videos possible,
and demonstrate that our robot is able to execute the learned collaborative
activities in real-time directly based on its camera input.
| Jangwon Lee and Michael S. Ryoo | null | 1703.0104 | null | null |
Adversarial Examples for Semantic Image Segmentation | stat.ML cs.CR cs.CV cs.LG cs.NE | Machine learning methods in general and Deep Neural Networks in particular
have shown to be vulnerable to adversarial perturbations. So far this
phenomenon has mainly been studied in the context of whole-image
classification. In this contribution, we analyse how adversarial perturbations
can affect the task of semantic segmentation. We show how existing adversarial
attackers can be transferred to this task and that it is possible to create
imperceptible adversarial perturbations that lead a deep network to misclassify
almost all pixels of a chosen class while leaving network prediction nearly
unchanged outside this class.
| Volker Fischer, Mummadi Chaithanya Kumar, Jan Hendrik Metzen, Thomas
Brox | null | 1703.01101 | null | null |
Differentially Private Bayesian Learning on Distributed Data | stat.ML cs.CR cs.LG stat.CO | Many applications of machine learning, for example in health care, would
benefit from methods that can guarantee privacy of data subjects. Differential
privacy (DP) has become established as a standard for protecting learning
results. The standard DP algorithms require a single trusted party to have
access to the entire data, which is a clear weakness. We consider DP Bayesian
learning in a distributed setting, where each party only holds a single sample
or a few samples of the data. We propose a learning strategy based on a secure
multi-party sum function for aggregating summaries from data holders and the
Gaussian mechanism for DP. Our method builds on an asymptotically optimal and
practically efficient DP Bayesian inference with rapidly diminishing extra
cost.
| Mikko Heikkil\"a and Eemil Lagerspetz and Samuel Kaski and Kana
Shimizu and Sasu Tarkoma and Antti Honkela | null | 1703.01106 | null | null |
On the Behavior of Convolutional Nets for Feature Extraction | cs.NE cs.AI cs.LG stat.ML | Deep neural networks are representation learning techniques. During training,
a deep net is capable of generating a descriptive language of unprecedented
size and detail in machine learning. Extracting the descriptive language coded
within a trained CNN model (in the case of image data), and reusing it for
other purposes is a field of interest, as it provides access to the visual
descriptors previously learnt by the CNN after processing millions of images,
without requiring an expensive training phase. Contributions to this field
(commonly known as feature representation transfer or transfer learning) have
been purely empirical so far, extracting all CNN features from a single layer
close to the output and testing their performance by feeding them to a
classifier. This approach has provided consistent results, although its
relevance is limited to classification tasks. In a completely different
approach, in this paper we statistically measure the discriminative power of
every single feature found within a deep CNN, when used for characterizing
every class of 11 datasets. We seek to provide new insights into the behavior
of CNN features, particularly the ones from convolutional layers, as this can
be relevant for their application to knowledge representation and reasoning.
Our results confirm that low and middle level features may behave differently
to high level features, but only under certain conditions. We find that all CNN
features can be used for knowledge representation purposes both by their
presence or by their absence, doubling the information a single CNN feature may
provide. We also study how much noise these features may include, and propose a
thresholding approach to discard most of it. All these insights have a direct
application to the generation of CNN embedding spaces.
| Dario Garcia-Gasulla, Ferran Par\'es, Armand Vilalta, Jonatan Moreno,
Eduard Ayguad\'e, Jes\'us Labarta, Ulises Cort\'es, Toyotaro Suzumura | null | 1703.01127 | null | null |
Dynamic State Warping | cs.LG | The ubiquity of sequences in many domains enhances significant recent
interest in sequence learning, for which a basic problem is how to measure the
distance between sequences. Dynamic time warping (DTW) aligns two sequences by
nonlinear local warping and returns a distance value. DTW shows superior
ability in many applications, e.g. video, image, etc. However, in DTW, two
points are paired essentially based on point-to-point Euclidean distance (ED)
without considering the autocorrelation of sequences. Thus, points with
different semantic meanings, e.g. peaks and valleys, may be matched providing
their coordinate values are similar. As a result, DTW is sensitive to noise and
poorly interpretable. This paper proposes an efficient and flexible sequence
alignment algorithm, dynamic state warping (DSW). DSW converts each time point
into a latent state, which endows point-wise autocorrelation information.
Alignment is performed by using the state sequences. Thus DSW is able to yield
alignment that is semantically more interpretable than that of DTW. Using one
nearest neighbor classifier, DSW shows significant improvement on
classification accuracy in comparison to ED (70/85 wins) and DTW (74/85 wins).
We also empirically demonstrate that DSW is more robust and scales better to
long sequences than ED and DTW.
| Zhichen Gong, Huanhuan Chen | null | 1703.01141 | null | null |
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and
Sample Complexity | cs.LG stat.ML | Learning the directed acyclic graph (DAG) structure of a Bayesian network
from observational data is a notoriously difficult problem for which many
hardness results are known. In this paper we propose a provably polynomial-time
algorithm for learning sparse Gaussian Bayesian networks with equal noise
variance --- a class of Bayesian networks for which the DAG structure can be
uniquely identified from observational data --- under high-dimensional
settings. We show that $O(k^4 \log p)$ number of samples suffices for our
method to recover the true DAG structure with high probability, where $p$ is
the number of variables and $k$ is the maximum Markov blanket size. We obtain
our theoretical guarantees under a condition called Restricted Strong Adjacency
Faithfulness, which is strictly weaker than strong faithfulness --- a condition
that other methods based on conditional independence testing need for their
success. The sample complexity of our method matches the information-theoretic
limits in terms of the dependence on $p$. We show that our method out-performs
existing state-of-the-art methods for learning Gaussian Bayesian networks in
terms of recovering the true DAG structure while being comparable in speed to
heuristic methods.
| Asish Ghoshal and Jean Honorio | null | 1703.01196 | null | null |
Stochastic Separation Theorems | cs.LG cs.AI | The problem of non-iterative one-shot and non-destructive correction of
unavoidable mistakes arises in all Artificial Intelligence applications in the
real world. Its solution requires robust separation of samples with errors from
samples where the system works properly. We demonstrate that in (moderately)
high dimension this separation could be achieved with probability close to one
by linear discriminants. Surprisingly, separation of a new image from a very
large set of known images is almost always possible even in moderately high
dimensions by linear functionals, and coefficients of these functionals can be
found explicitly. Based on fundamental properties of measure concentration, we
show that for $M<a\exp(b{n})$ random $M$-element sets in $\mathbb{R}^n$ are
linearly separable with probability $p$, $p>1-\vartheta$, where $1>\vartheta>0$
is a given small constant. Exact values of $a,b>0$ depend on the probability
distribution that determines how the random $M$-element sets are drawn, and on
the constant $\vartheta$. These {\em stochastic separation theorems} provide a
new instrument for the development, analysis, and assessment of machine
learning methods and algorithms in high dimension. Theoretical statements are
illustrated with numerical examples.
| A.N. Gorban, I.Y. Tyukin | 10.1016/j.neunet.2017.07.014 | 1703.01203 | null | null |
Learning Graphical Games from Behavioral Data: Sufficient and Necessary
Conditions | cs.LG | In this paper we obtain sufficient and necessary conditions on the number of
samples required for exact recovery of the pure-strategy Nash equilibria (PSNE)
set of a graphical game from noisy observations of joint actions. We consider
sparse linear influence games --- a parametric class of graphical games with
linear payoffs, and represented by directed graphs of n nodes (players) and
in-degree of at most k. We show that one can efficiently recover the PSNE set
of a linear influence game with $O(k^2 \log n)$ samples, under very general
observation models. On the other hand, we show that $\Omega(k \log n)$ samples
are necessary for any procedure to recover the PSNE set from observations of
joint actions.
| Asish Ghoshal and Jean Honorio | null | 1703.01218 | null | null |
Denoising Adversarial Autoencoders | cs.CV cs.LG stat.ML | Unsupervised learning is of growing interest because it unlocks the potential
held in vast amounts of unlabelled data to learn useful representations for
inference. Autoencoders, a form of generative model, may be trained by learning
to reconstruct unlabelled input data from a latent representation space. More
robust representations may be produced by an autoencoder if it learns to
recover clean input samples from corrupted ones. Representations may be further
improved by introducing regularisation during training to shape the
distribution of the encoded data in latent space. We suggest denoising
adversarial autoencoders, which combine denoising and regularisation, shaping
the distribution of latent space using adversarial training. We introduce a
novel analysis that shows how denoising may be incorporated into the training
and sampling of adversarial autoencoders. Experiments are performed to assess
the contributions that denoising makes to the learning of representations for
classification and sample synthesis. Our results suggest that autoencoders
trained using a denoising criterion achieve higher classification performance,
and can synthesise samples that are more consistent with the input data than
those trained without a corruption process.
| Antonia Creswell, Anil Anthony Bharath | null | 1703.0122 | null | null |
Virtual vs. Real: Trading Off Simulations and Physical Experiments in
Reinforcement Learning with Bayesian Optimization | cs.RO cs.LG cs.SY | In practice, the parameters of control policies are often tuned manually.
This is time-consuming and frustrating. Reinforcement learning is a promising
alternative that aims to automate this process, yet often requires too many
experiments to be practical. In this paper, we propose a solution to this
problem by exploiting prior knowledge from simulations, which are readily
available for most robotic platforms. Specifically, we extend Entropy Search, a
Bayesian optimization algorithm that maximizes information gain from each
experiment, to the case of multiple information sources. The result is a
principled way to automatically combine cheap, but inaccurate information from
simulations with expensive and accurate physical experiments in a
cost-effective manner. We apply the resulting method to a cart-pole system,
which confirms that the algorithm can find good control policies with fewer
experiments than standard Bayesian optimization on the physical system only.
| Alonso Marco, Felix Berkenkamp, Philipp Hennig, Angela P. Schoellig,
Andreas Krause, Stefan Schaal, Sebastian Trimpe | 10.1109/ICRA.2017.7989186 | 1703.0125 | null | null |
Machine Learning on Sequential Data Using a Recurrent Weighted Average | stat.ML cs.LG | Recurrent Neural Networks (RNN) are a type of statistical model designed to
handle sequential data. The model reads a sequence one symbol at a time. Each
symbol is processed based on information collected from the previous symbols.
With existing RNN architectures, each symbol is processed using only
information from the previous processing step. To overcome this limitation, we
propose a new kind of RNN model that computes a recurrent weighted average
(RWA) over every past processing step. Because the RWA can be computed as a
running average, the computational overhead scales like that of any other RNN
architecture. The approach essentially reformulates the attention mechanism
into a stand-alone model. The performance of the RWA model is assessed on the
variable copy problem, the adding problem, classification of artificial
grammar, classification of sequences by length, and classification of the MNIST
images (where the pixels are read sequentially one at a time). On almost every
task, the RWA model is found to outperform a standard LSTM model.
| Jared Ostmeyer and Lindsay Cowell | 10.1016/j.neucom.2018.11.066 | 1703.01253 | null | null |
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning | cs.LG | Deep reinforcement learning algorithms have been shown to learn complex tasks
using highly general policy classes. However, sparse reward problems remain a
significant challenge. Exploration methods based on novelty detection have been
particularly successful in such settings but typically require generative or
predictive models of the observations, which can be difficult to train when the
observations are very high-dimensional and complex, as in the case of raw
images. We propose a novelty detection algorithm for exploration that is based
entirely on discriminatively trained exemplar models, where classifiers are
trained to discriminate each visited state against all others. Intuitively,
novel states are easier to distinguish against other states seen during
training. We show that this kind of discriminative modeling corresponds to
implicit density estimation, and that it can be combined with count-based
exploration to produce competitive results on a range of popular benchmark
tasks, including state-of-the-art results on challenging egocentric
observations in the vizDoom benchmark.
| Justin Fu and John D. Co-Reyes, and Sergey Levine | null | 1703.0126 | null | null |
Multi-step Reinforcement Learning: A Unifying Algorithm | cs.AI cs.LG | Unifying seemingly disparate algorithmic ideas to produce better performing
algorithms has been a longstanding goal in reinforcement learning. As a primary
example, TD($\lambda$) elegantly unifies one-step TD prediction with Monte
Carlo methods through the use of eligibility traces and the trace-decay
parameter $\lambda$. Currently, there are a multitude of algorithms that can be
used to perform TD control, including Sarsa, $Q$-learning, and Expected Sarsa.
These methods are often studied in the one-step case, but they can be extended
across multiple time steps to achieve better performance. Each of these
algorithms is seemingly distinct, and no one dominates the others for all
problems. In this paper, we study a new multi-step action-value algorithm
called $Q(\sigma)$ which unifies and generalizes these existing algorithms,
while subsuming them as special cases. A new parameter, $\sigma$, is introduced
to allow the degree of sampling performed by the algorithm at each step during
its backup to be continuously varied, with Sarsa existing at one extreme (full
sampling), and Expected Sarsa existing at the other (pure expectation).
$Q(\sigma)$ is generally applicable to both on- and off-policy learning, but in
this work we focus on experiments in the on-policy case. Our results show that
an intermediate value of $\sigma$, which results in a mixture of the existing
algorithms, performs better than either extreme. The mixture can also be varied
dynamically which can result in even greater performance.
| Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias
Holland, Richard S. Sutton | null | 1703.01327 | null | null |
Generative Poisoning Attack Method Against Neural Networks | cs.CR cs.LG stat.ML | Poisoning attack is identified as a severe security threat to machine
learning algorithms. In many applications, for example, deep neural network
(DNN) models collect public data as the inputs to perform re-training, where
the input data can be poisoned. Although poisoning attack against support
vector machines (SVM) has been extensively studied before, there is still very
limited knowledge about how such attack can be implemented on neural networks
(NN), especially DNNs. In this work, we first examine the possibility of
applying traditional gradient-based method (named as the direct gradient
method) to generate poisoned data against NNs by leveraging the gradient of the
target model w.r.t. the normal data. We then propose a generative method to
accelerate the generation rate of the poisoned data: an auto-encoder
(generator) used to generate poisoned data is updated by a reward function of
the loss, and the target NN model (discriminator) receives the poisoned data to
calculate the loss w.r.t. the normal data. Our experiment results show that the
generative method can speed up the poisoned data generation rate by up to
239.38x compared with the direct gradient method, with slightly lower model
accuracy degradation. A countermeasure is also designed to detect such
poisoning attack methods by checking the loss of the target model.
| Chaofei Yang, Qing Wu, Hai Li, Yiran Chen | null | 1703.0134 | null | null |
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles | cs.AI cs.LG stat.ML | We study contextual linear bandit problems under feature uncertainty; they
are noisy with missing entries. To address the challenges of the noise, we
analyze Bayesian oracles given observed noisy features. Our Bayesian analysis
finds that the optimal hypothesis can be far from the underlying realizability
function, depending on the noise characteristics, which are highly
non-intuitive and do not occur for classical noiseless setups. This implies
that classical approaches cannot guarantee a non-trivial regret bound.
Therefore, we propose an algorithm that aims at the Bayesian oracle from
observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret
bound when there is a large number of arms. We demonstrate the proposed
algorithm using synthetic and real-world datasets.
| Jung-hun Kim and Se-Young Yun and Minchan Jeong and Jun Hyun Nam and
Jinwoo Shin and Richard Combes | null | 1703.01347 | null | null |
Convex Geometry of the Generalized Matrix-Fractional Function | math.OC cs.LG stat.ML | Generalized matrix-fractional (GMF) functions are a class of matrix support
functions introduced by Burke and Hoheisel as a tool for unifying a range of
seemingly divergent matrix optimization problems associated with inverse
problems, regularization and learning. In this paper we dramatically simplify
the support function representation for GMF functions as well as the
representation of their subdifferentials. These new representations allow the
ready computation of a range of important related geometric objects whose
formulations were previously unavailable.
| James V. Burke, Yuan Gao, Tim Hoheisel | null | 1703.01363 | null | null |
Axiomatic Attribution for Deep Networks | cs.LG | We study the problem of attributing the prediction of a deep network to its
input features, a problem previously studied by several other works. We
identify two fundamental axioms---Sensitivity and Implementation Invariance
that attribution methods ought to satisfy. We show that they are not satisfied
by most known attribution methods, which we consider to be a fundamental
weakness of those methods. We use the axioms to guide the design of a new
attribution method called Integrated Gradients. Our method requires no
modification to the original network and is extremely simple to implement; it
just needs a few calls to the standard gradient operator. We apply this method
to a couple of image models, a couple of text models and a chemistry model,
demonstrating its ability to debug networks, to extract rules from a network,
and to enable users to engage with models better.
| Mukund Sundararajan, Ankur Taly, Qiqi Yan | null | 1703.01365 | null | null |
Recurrent Poisson Factorization for Temporal Recommendation | cs.SI cs.LG stat.ML | Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms.
| Seyed Abbas Hosseini, Keivan Alizadeh, Ali Khodadadi, Ali Arabzadeh,
Mehrdad Farajtabar, Hongyuan Zha, Hamid R. Rabiee | null | 1703.01442 | null | null |
Learning Deep Matrix Representations | cs.LG | We present a new distributed representation in deep neural nets wherein the
information is represented in native form as a matrix. This differs from
current neural architectures that rely on vector representations. We consider
matrices as central to the architecture and they compose the input, hidden and
output layers. The model representation is more compact and elegant -- the
number of parameters grows only with the largest dimension of the incoming
layer rather than the number of hidden units. We derive several new deep
networks: (i) feed-forward nets that map an input matrix into an output matrix,
(ii) recurrent nets which map a sequence of input matrices into a sequence of
output matrices. We also reinterpret existing models for (iii) memory-augmented
networks and (iv) graphs using matrix notations. For graphs we demonstrate how
the new notations lead to simple but effective extensions with multiple
attentions. Extensive experiments on handwritten digits recognition, face
reconstruction, sequence to sequence learning, EEG classification, and
graph-based node classification demonstrate the efficacy and compactness of the
matrix architectures.
| Kien Do, Truyen Tran, Svetha Venkatesh | null | 1703.01454 | null | null |
Chain-NN: An Energy-Efficient 1D Chain Architecture for Accelerating
Deep Convolutional Neural Networks | cs.AR cs.LG | Deep convolutional neural networks (CNN) have shown their good performances
in many computer vision tasks. However, the high computational complexity of
CNN involves a huge amount of data movements between the computational
processor core and memory hierarchy which occupies the major of the power
consumption. This paper presents Chain-NN, a novel energy-efficient 1D chain
architecture for accelerating deep CNNs. Chain-NN consists of the dedicated
dual-channel process engines (PE). In Chain-NN, convolutions are done by the 1D
systolic primitives composed of a group of adjacent PEs. These systolic
primitives, together with the proposed column-wise scan input pattern, can
fully reuse input operand to reduce the memory bandwidth requirement for energy
saving. Moreover, the 1D chain architecture allows the systolic primitives to
be easily reconfigured according to specific CNN parameters with fewer design
complexity. The synthesis and layout of Chain-NN is under TSMC 28nm process. It
costs 3751k logic gates and 352KB on-chip memory. The results show a 576-PE
Chain-NN can be scaled up to 700MHz. This achieves a peak throughput of
806.4GOPS with 567.5mW and is able to accelerate the five convolutional layers
in AlexNet at a frame rate of 326.2fps. 1421.0GOPS/W power efficiency is at
least 2.5 to 4.1x times better than the state-of-the-art works.
| Shihao Wang, Dajiang Zhou, Xushen Han, Takeshi Yoshimura | null | 1703.01457 | null | null |
Adversarial Generation of Real-time Feedback with Neural Networks for
Simulation-based Training | cs.LG cs.HC stat.ML | Simulation-based training (SBT) is gaining popularity as a low-cost and
convenient training technique in a vast range of applications. However, for a
SBT platform to be fully utilized as an effective training tool, it is
essential that feedback on performance is provided automatically in real-time
during training. It is the aim of this paper to develop an efficient and
effective feedback generation method for the provision of real-time feedback in
SBT. Existing methods either have low effectiveness in improving novice skills
or suffer from low efficiency, resulting in their inability to be used in
real-time. In this paper, we propose a neural network based method to generate
feedback using the adversarial technique. The proposed method utilizes a
bounded adversarial update to minimize a L1 regularized loss via
back-propagation. We empirically show that the proposed method can be used to
generate simple, yet effective feedback. Also, it was observed to have high
effectiveness and efficiency when compared to existing methods, thus making it
a promising option for real-time feedback generation in SBT.
| Xingjun Ma, Sudanthi Wijewickrema, Shuo Zhou, Yun Zhou, Zakaria
Mhammedi, Stephen O'Leary, James Bailey | null | 1703.0146 | null | null |
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain
Adaptation | cs.RO cs.LG | Appearance changes due to weather and seasonal conditions represent a strong
impediment to the robust implementation of machine learning systems in outdoor
robotics. While supervised learning optimises a model for the training domain,
it will deliver degraded performance in application domains that underlie
distributional shifts caused by these changes. Traditionally, this problem has
been addressed via the collection of labelled data in multiple domains or by
imposing priors on the type of shift between both domains. We frame the problem
in the context of unsupervised domain adaptation and develop a framework for
applying adversarial techniques to adapt popular, state-of-the-art network
architectures with the additional objective to align features across domains.
Moreover, as adversarial training is notoriously unstable, we first perform an
extensive ablation study, adapting many techniques known to stabilise
generative adversarial networks, and evaluate on a surrogate classification
task with the same appearance change. The distilled insights are applied to the
problem of free-space segmentation for motion planning in autonomous driving.
| Markus Wulfmeier, Alex Bewley and Ingmar Posner | null | 1703.01461 | null | null |
Sharp bounds for population recovery | cs.DS cs.LG math.ST stat.TH | The population recovery problem is a basic problem in noisy unsupervised
learning that has attracted significant research attention in recent years
[WY12,DRWY12, MS13, BIMP13, LZ15,DST16]. A number of different variants of this
problem have been studied, often under assumptions on the unknown distribution
(such as that it has restricted support size). In this work we study the sample
complexity and algorithmic complexity of the most general version of the
problem, under both bit-flip noise and erasure noise model. We give essentially
matching upper and lower sample complexity bounds for both noise models, and
efficient algorithms matching these sample complexity bounds up to polynomial
factors.
| Anindya De and Ryan O'Donnell and Rocco Servedio | null | 1703.01474 | null | null |
Machine Learning Friendly Set Version of Johnson-Lindenstrauss Lemma | cs.DS cs.LG | In this paper we make a novel use of the Johnson-Lindenstrauss Lemma. The
Lemma has an existential form saying that there exists a JL transformation $f$
of the data points into lower dimensional space such that all of them fall into
predefined error range $\delta$.
We formulate in this paper a theorem stating that we can choose the target
dimensionality in a random projection type JL linear transformation in such a
way that with probability $1-\epsilon$ all of them fall into predefined error
range $\delta$ for any user-predefined failure probability $\epsilon$.
This result is important for applications such a data clustering where we
want to have a priori dimensionality reducing transformation instead of trying
out a (large) number of them, as with traditional Johnson-Lindenstrauss Lemma.
In particular, we take a closer look at the $k$-means algorithm and prove that
a good solution in the projected space is also a good solution in the original
space. Furthermore, under proper assumptions local optima in the original space
are also ones in the projected space. We define also conditions for which
clusterability property of the original space is transmitted to the projected
space, so that special case algorithms for the original space are also
applicable in the projected space.
| Mieczys{\l}aw A. K{\l}opotek | null | 1703.01507 | null | null |
Using Graphs of Classifiers to Impose Declarative Constraints on
Semi-supervised Learning | cs.LG cs.CL stat.ML | We propose a general approach to modeling semi-supervised learning (SSL)
algorithms. Specifically, we present a declarative language for modeling both
traditional supervised classification tasks and many SSL heuristics, including
both well-known heuristics such as co-training and novel domain-specific
heuristics. In addition to representing individual SSL heuristics, we show that
multiple heuristics can be automatically combined using Bayesian optimization
methods. We experiment with two classes of tasks, link-based text
classification and relation extraction. We show modest improvements on
well-studied link-based classification benchmarks, and state-of-the-art results
on relation-extraction tasks for two realistic domains.
| Lidong Bing and William W. Cohen and Bhuwan Dhingra | null | 1703.01557 | null | null |
LR-GAN: Layered Recursive Generative Adversarial Networks for Image
Generation | cs.CV cs.LG | We present LR-GAN: an adversarial image generation model which takes scene
structure and context into account. Unlike previous generative adversarial
networks (GANs), the proposed GAN learns to generate image background and
foregrounds separately and recursively, and stitch the foregrounds on the
background in a contextually relevant manner to produce a complete natural
image. For each foreground, the model learns to generate its appearance, shape
and pose. The whole model is unsupervised, and is trained in an end-to-end
manner with gradient descent methods. The experiments demonstrate that LR-GAN
can generate more natural images with objects that are more human recognizable
than DCGAN.
| Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh | null | 1703.0156 | null | null |
Graph sampling with determinantal processes | cs.LG cs.SI stat.ML | We present a new random sampling strategy for k-bandlimited signals defined
on graphs, based on determinantal point processes (DPP). For small graphs, ie,
in cases where the spectrum of the graph is accessible, we exhibit a DPP
sampling scheme that enables perfect recovery of bandlimited signals. For large
graphs, ie, in cases where the graph's spectrum is not accessible, we
investigate, both theoretically and empirically, a sub-optimal but much faster
DPP based on loop-erased random walks on the graph. Preliminary experiments
show promising results especially in cases where the number of measurements
should stay as small as possible and for graphs that have a strong community
structure. Our sampling scheme is efficient and can be applied to graphs with
up to $10^6$ nodes.
| Nicolas Tremblay, Pierre-Olivier Amblard, Simon Barthelm\'e | null | 1703.01594 | null | null |
A Theory of Output-Side Unsupervised Domain Adaptation | cs.LG stat.ML | When learning a mapping from an input space to an output space, the
assumption that the sample distribution of the training data is the same as
that of the test data is often violated. Unsupervised domain shift methods
adapt the learned function in order to correct for this shift. Previous work
has focused on utilizing unlabeled samples from the target distribution. We
consider the complementary problem in which the unlabeled samples are given
post mapping, i.e., we are given the outputs of the mapping of unknown samples
from the shifted domain. Two other variants are also studied: the two sided
version, in which unlabeled samples are give from both the input and the output
spaces, and the Domain Transfer problem, which was recently formalized. In all
cases, we derive generalization bounds that employ discrepancy terms.
| Tomer Galanti, Lior Wolf | null | 1703.01606 | null | null |
Improving Regret Bounds for Combinatorial Semi-Bandits with
Probabilistically Triggered Arms and Its Applications | cs.LG stat.ML | We study combinatorial multi-armed bandit with probabilistically triggered
arms (CMAB-T) and semi-bandit feedback. We resolve a serious issue in the prior
CMAB-T studies where the regret bounds contain a possibly exponentially large
factor of $1/p^*$, where $p^*$ is the minimum positive probability that an arm
is triggered by any action. We address this issue by introducing a triggering
probability modulated (TPM) bounded smoothness condition into the general
CMAB-T framework, and show that many applications such as influence
maximization bandit and combinatorial cascading bandit satisfy this TPM
condition. As a result, we completely remove the factor of $1/p^*$ from the
regret bounds, achieving significantly better regret bounds for influence
maximization and cascading bandits than before. Finally, we provide lower bound
results showing that the factor $1/p^*$ is unavoidable for general CMAB-T
problems, suggesting that the TPM condition is crucial in removing this factor.
| Qinshi Wang and Wei Chen | null | 1703.0161 | null | null |
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial | cs.CL cs.LG stat.ML | This tutorial introduces a new and powerful set of techniques variously
called "neural machine translation" or "neural sequence-to-sequence models".
These techniques have been used in a number of tasks regarding the handling of
human language, and can be a powerful tool in the toolbox of anyone who wants
to model sequential data of some sort. The tutorial assumes that the reader
knows the basics of math and programming, but does not assume any particular
experience with neural networks or natural language processing. It attempts to
explain the intuition behind the various methods covered, then delves into them
with enough mathematical detail to understand them concretely, and culiminates
with a suggestion for an implementation exercise, where readers can test that
they understood the content in practice.
| Graham Neubig | null | 1703.01619 | null | null |
Data-Dependent Stability of Stochastic Gradient Descent | cs.LG | We establish a data-dependent notion of algorithmic stability for Stochastic
Gradient Descent (SGD), and employ it to develop novel generalization bounds.
This is in contrast to previous distribution-free algorithmic stability results
for SGD which depend on the worst-case constants. By virtue of the
data-dependent argument, our bounds provide new insights into learning with SGD
on convex and non-convex problems. In the convex case, we show that the bound
on the generalization error depends on the risk at the initialization point. In
the non-convex case, we prove that the expected curvature of the objective
function around the initialization point has crucial influence on the
generalization error. In both cases, our results suggest a simple data-driven
strategy to stabilize SGD by pre-screening its initialization. As a corollary,
our results allow us to show optimistic generalization bounds that exhibit fast
convergence rates for SGD subject to a vanishing empirical risk and low noise
of stochastic gradient.
| Ilja Kuzborskij, Christoph H. Lampert | null | 1703.01678 | null | null |
Multi-Objective Non-parametric Sequential Prediction | cs.LG | Online-learning research has mainly been focusing on minimizing one objective
function. In many real-world applications, however, several objective functions
have to be considered simultaneously. Recently, an algorithm for dealing with
several objective functions in the i.i.d. case has been presented. In this
paper, we extend the multi-objective framework to the case of stationary and
ergodic processes, thus allowing dependencies among observations. We first
identify an asymptomatic lower bound for any prediction strategy and then
present an algorithm whose predictions achieve the optimal solution while
fulfilling any continuous and convex constraining criterion.
| Guy Uziel and Ran El-Yaniv | null | 1703.0168 | null | null |
Third-Person Imitation Learning | cs.LG | Reinforcement learning (RL) makes it possible to train agents capable of
achieving sophisticated goals in complex and uncertain environments. A key
difficulty in reinforcement learning is specifying a reward function for the
agent to optimize. Traditionally, imitation learning in RL has been used to
overcome this problem. Unfortunately, hitherto imitation learning methods tend
to require that demonstrations are supplied in the first-person: the agent is
provided with a sequence of states and a specification of the actions that it
should have taken. While powerful, this kind of imitation learning is limited
by the relatively hard problem of collecting first-person demonstrations.
Humans address this problem by learning from third-person demonstrations: they
observe other humans perform tasks, infer the task, and accomplish the same
task themselves.
In this paper, we present a method for unsupervised third-person imitation
learning. Here third-person refers to training an agent to correctly achieve a
simple goal in a simple environment when it is provided a demonstration of a
teacher achieving the same goal but from a different viewpoint; and
unsupervised refers to the fact that the agent receives only these third-person
demonstrations, and is not provided a correspondence between teacher states and
student states. Our methods primary insight is that recent advances from domain
confusion can be utilized to yield domain agnostic features which are crucial
during the training process. To validate our approach, we report successful
experiments on learning from third-person demonstrations in a pointmass domain,
a reacher domain, and inverted pendulum.
| Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever | null | 1703.01703 | null | null |
Measuring Sample Quality with Kernels | stat.ML cs.LG | Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid
sampling at the cost of more biased inference. Since standard MCMC diagnostics
fail to detect these biases, researchers have developed computable Stein
discrepancy measures that provably determine the convergence of a sample to its
target distribution. This approach was recently combined with the theory of
reproducing kernels to define a closed-form kernel Stein discrepancy (KSD)
computable by summing kernel evaluations across pairs of sample points. We
develop a theory of weak convergence for KSDs based on Stein's method,
demonstrate that commonly used KSDs fail to detect non-convergence even for
Gaussian targets, and show that kernels with slowly decaying tails provably
determine convergence for a large class of target distributions. The resulting
convergence-determining KSDs are suitable for comparing biased, exact, and
deterministic sample sequences and simpler to compute and parallelize than
alternative Stein discrepancies. We use our tools to compare biased samplers,
select sampler hyperparameters, and improve upon existing KSD approaches to
one-sample hypothesis testing and sample quality improvement.
| Jackson Gorham and Lester Mackey | null | 1703.01717 | null | null |
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning | cs.LG | Exploration in complex domains is a key challenge in reinforcement learning,
especially for tasks with very sparse rewards. Recent successes in deep
reinforcement learning have been achieved mostly using simple heuristic
exploration strategies such as $\epsilon$-greedy action selection or Gaussian
control noise, but there are many tasks where these methods are insufficient to
make any learning progress. Here, we consider more complex heuristics:
efficient and scalable exploration strategies that maximize a notion of an
agent's surprise about its experiences via intrinsic motivation. We propose to
learn a model of the MDP transition probabilities concurrently with the policy,
and to form intrinsic rewards that approximate the KL-divergence of the true
transition probabilities from the learned model. One of our approximations
results in using surprisal as intrinsic motivation, while the other gives the
$k$-step learning progress. We show that our incentives enable agents to
succeed in a wide range of environments with high-dimensional state spaces and
very sparse rewards, including continuous control tasks and games in the Atari
RAM domain, outperforming several other heuristic exploration techniques.
| Joshua Achiam, Shankar Sastry | null | 1703.01732 | null | null |
A Correlative Denoising Autoencoder to Model Social Influence for Top-N
Recommender System | cs.IR cs.LG cs.SI | In recent years, there are numerous works been proposed to leverage the
techniques of deep learning to improve social-aware recommendation performance.
In most cases, it requires a larger number of data to train a robust deep
learning model, which contains a lot of parameters to fit training data.
However, both data of user ratings and social networks are facing critical
sparse problem, which makes it not easy to train a robust deep neural network
model. Towards this problem, we propose a novel Correlative Denoising
Autoencoder (CoDAE) method by taking correlations between users with multiple
roles into account to learn robust representations from sparse inputs of
ratings and social networks for recommendation. We develop the CoDAE model by
utilizing three separated autoencoders to learn user features with roles of
rater, truster and trustee, respectively. Especially, on account of that each
input unit of user vectors with roles of truster and trustee is corresponding
to a particular user, we propose to utilize shared parameters to learn common
information of the units that corresponding to same users. Moreover, we propose
a related regularization term to learn correlations between user features that
learnt by the three subnetworks of CoDAE model. We further conduct a series of
experiments to evaluate the proposed method on two public datasets for Top-N
recommendation task. The experimental results demonstrate that the proposed
model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP
and NDCG.
| Yiteng Pan, Fazhi He, Haiping Yu | 10.1007/s11704-019-8123-3 | 1703.0176 | null | null |
Building a Regular Decision Boundary with Deep Networks | cs.CV cs.LG | In this work, we build a generic architecture of Convolutional Neural
Networks to discover empirical properties of neural networks. Our first
contribution is to introduce a state-of-the-art framework that depends upon few
hyper parameters and to study the network when we vary them. It has no max
pooling, no biases, only 13 layers, is purely convolutional and yields up to
95.4% and 79.6% accuracy respectively on CIFAR10 and CIFAR100. We show that the
nonlinearity of a deep network does not need to be continuous, non expansive or
point-wise, to achieve good performance. We show that increasing the width of
our network permits being competitive with very deep networks. Our second
contribution is an analysis of the contraction and separation properties of
this network. Indeed, a 1-nearest neighbor classifier applied on deep features
progressively improves with depth, which indicates that the representation is
progressively more regular. Besides, we defined and analyzed local support
vectors that separate classes locally. All our experiments are reproducible and
code is available online, based on TensorFlow.
| Edouard Oyallon | null | 1703.01775 | null | null |
Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results | cs.NE cs.LG stat.ML | The recently proposed Temporal Ensembling has achieved state-of-the-art
results in several semi-supervised learning benchmarks. It maintains an
exponential moving average of label predictions on each training example, and
penalizes predictions that are inconsistent with this target. However, because
the targets change only once per epoch, Temporal Ensembling becomes unwieldy
when learning large datasets. To overcome this problem, we propose Mean
Teacher, a method that averages model weights instead of label predictions. As
an additional benefit, Mean Teacher improves test accuracy and enables training
with fewer labels than Temporal Ensembling. Without changing the network
architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250
labels, outperforming Temporal Ensembling trained with 1000 labels. We also
show that a good network architecture is crucial to performance. Combining Mean
Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with
4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels
from 35.24% to 9.11%.
| Antti Tarvainen and Harri Valpola | null | 1703.0178 | null | null |
Sample-level Deep Convolutional Neural Networks for Music Auto-tagging
Using Raw Waveforms | cs.SD cs.LG cs.MM cs.NE | Recently, the end-to-end approach that learns hierarchical representations
from raw data using deep convolutional neural networks has been successfully
explored in the image, text and speech domains. This approach was applied to
musical signals as well but has been not fully explored yet. To this end, we
propose sample-level deep convolutional neural networks which learn
representations from very small grains of waveforms (e.g. 2 or 3 samples)
beyond typical frame-level input representations. Our experiments show how deep
architectures with sample-level filters improve the accuracy in music
auto-tagging and they provide results comparable to previous state-of-the-art
performances for the Magnatagatune dataset and Million Song Dataset. In
addition, we visualize filters learned in a sample-level DCNN in each layer to
identify hierarchically learned features and show that they are sensitive to
log-scaled frequency along layer, such as mel-frequency spectrogram that is
widely used in music classification systems.
| Jongpil Lee, Jiyoung Park, Keunhyoung Luke Kim, Juhan Nam | null | 1703.01789 | null | null |
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained
Convolutional Neural Networks for Music Auto-tagging | cs.NE cs.LG cs.MM cs.SD | Music auto-tagging is often handled in a similar manner to image
classification by regarding the 2D audio spectrogram as image data. However,
music auto-tagging is distinguished from image classification in that the tags
are highly diverse and have different levels of abstractions. Considering this
issue, we propose a convolutional neural networks (CNN)-based architecture that
embraces multi-level and multi-scaled features. The architecture is trained in
three steps. First, we conduct supervised feature learning to capture local
audio features using a set of CNNs with different input sizes. Second, we
extract audio features from each layer of the pre-trained convolutional
networks separately and aggregate them altogether given a long audio clip.
Finally, we put them into fully-connected networks and make final predictions
of the tags. Our experiments show that using the combination of multi-level and
multi-scale features is highly effective in music auto-tagging and the proposed
method outperforms previous state-of-the-arts on the MagnaTagATune dataset and
the Million Song Dataset. We further show that the proposed architecture is
useful in transfer learning.
| Jongpil Lee, Juhan Nam | 10.1109/LSP.2017.2713830 | 1703.01793 | null | null |
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition
Algorithm for Practical Use | cs.LG stat.ML | The popular Alternating Least Squares (ALS) algorithm for tensor
decomposition is efficient and easy to implement, but often converges to poor
local optima---particularly when the weights of the factors are non-uniform. We
propose a modification of the ALS approach that is as efficient as standard
ALS, but provably recovers the true factors with random initialization under
standard incoherence assumptions on the factors of the tensor. We demonstrate
the significant practical superiority of our approach over traditional ALS for
a variety of tasks on synthetic data---including tensor factorization on exact,
noisy and over-complete tensors, as well as tensor completion---and for
computing word embeddings from a third-order word tri-occurrence tensor.
| Vatsal Sharan, Gregory Valiant | null | 1703.01804 | null | null |
All You Need is Beyond a Good Init: Exploring Better Solution for
Training Extremely Deep Convolutional Neural Networks with Orthonormality and
Modulation | cs.CV cs.LG cs.NE | Deep neural network is difficult to train and this predicament becomes worse
as the depth increases. The essence of this problem exists in the magnitude of
backpropagated errors that will result in gradient vanishing or exploding
phenomenon. We show that a variant of regularizer which utilizes orthonormality
among different filter banks can alleviate this problem. Moreover, we design a
backward error modulation mechanism based on the quasi-isometry assumption
between two consecutive parametric layers. Equipped with these two ingredients,
we propose several novel optimization solutions that can be utilized for
training a specific-structured (repetitively triple modules of Conv-BNReLU)
extremely deep convolutional neural network (CNN) WITHOUT any shortcuts/
identity mappings from scratch. Experiments show that our proposed solutions
can achieve distinct improvements for a 44-layer and a 110-layer plain networks
on both the CIFAR-10 and ImageNet datasets. Moreover, we can successfully train
plain CNNs to match the performance of the residual counterparts.
Besides, we propose new principles for designing network structure from the
insights evoked by orthonormality. Combined with residual structure, we achieve
comparative performance on the ImageNet dataset.
| Di Xie and Jiang Xiong and Shiliang Pu | null | 1703.01827 | null | null |
Decomposable Submodular Function Minimization: Discrete and Continuous | cs.LG cs.DS | This paper investigates connections between discrete and continuous
approaches for decomposable submodular function minimization. We provide
improved running time estimates for the state-of-the-art continuous algorithms
for the problem using combinatorial arguments. We also provide a systematic
experimental comparison of the two types of methods, based on a clear
distinction between level-0 and level-1 algorithms.
| Alina Ene and Huy L. Nguyen and L\'aszl\'o A. V\'egh | null | 1703.0183 | null | null |
Generative and Discriminative Text Classification with Recurrent Neural
Networks | stat.ML cs.CL cs.LG | We empirically characterize the performance of discriminative and generative
LSTM models for text classification. We find that although RNN-based generative
models are more powerful than their bag-of-words ancestors (e.g., they account
for conditional dependencies across words in a document), they have higher
asymptotic error rates than discriminatively trained RNN models. However we
also find that generative models approach their asymptotic error rate more
rapidly than their discriminative counterparts---the same pattern that Ng &
Jordan (2001) proved holds for linear classification models that make more
naive conditional independence assumptions. Building on this finding, we
hypothesize that RNN-based generative classification models will be more robust
to shifts in the data distribution. This hypothesis is confirmed in a series of
experiments in zero-shot and continual learning settings that show that
generative models substantially outperform discriminative models.
| Dani Yogatama, Chris Dyer, Wang Ling, Phil Blunsom | null | 1703.01898 | null | null |
Near-Optimal Closeness Testing of Discrete Histogram Distributions | cs.DS cs.IT cs.LG math.IT math.ST stat.TH | We investigate the problem of testing the equivalence between two discrete
histograms. A {\em $k$-histogram} over $[n]$ is a probability distribution that
is piecewise constant over some set of $k$ intervals over $[n]$. Histograms
have been extensively studied in computer science and statistics. Given a set
of samples from two $k$-histogram distributions $p, q$ over $[n]$, we want to
distinguish (with high probability) between the cases that $p = q$ and
$\|p-q\|_1 \geq \epsilon$. The main contribution of this paper is a new
algorithm for this testing problem and a nearly matching information-theoretic
lower bound. Specifically, the sample complexity of our algorithm matches our
lower bound up to a logarithmic factor, improving on previous work by
polynomial factors in the relevant parameters. Our algorithmic approach applies
in a more general setting and yields improved sample upper bounds for testing
closeness of other structured distributions as well.
| Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin | null | 1703.01913 | null | null |
Metric Learning for Generalizing Spatial Relations to New Objects | cs.RO cs.AI cs.LG | Human-centered environments are rich with a wide variety of spatial relations
between everyday objects. For autonomous robots to operate effectively in such
environments, they should be able to reason about these relations and
generalize them to objects with different shapes and sizes. For example, having
learned to place a toy inside a basket, a robot should be able to generalize
this concept using a spoon and a cup. This requires a robot to have the
flexibility to learn arbitrary relations in a lifelong manner, making it
challenging for an expert to pre-program it with sufficient knowledge to do so
beforehand. In this paper, we address the problem of learning spatial relations
by introducing a novel method from the perspective of distance metric learning.
Our approach enables a robot to reason about the similarity between pairwise
spatial relations, thereby enabling it to use its previous knowledge when
presented with a new relation to imitate. We show how this makes it possible to
learn arbitrary spatial relations from non-expert users using a small number of
examples and in an interactive manner. Our extensive evaluation with real-world
data demonstrates the effectiveness of our method in reasoning about a
continuous spectrum of spatial relations and generalizing them to new objects.
| Oier Mees, Nichola Abdo, Mladen Mazuran, Wolfram Burgard | null | 1703.01946 | null | null |
Network Inference via the Time-Varying Graphical Lasso | cs.LG cs.SI math.OC | Many important problems can be modeled as a system of interconnected
entities, where each entity is recording time-dependent observations or
measurements. In order to spot trends, detect anomalies, and interpret the
temporal dynamics of such data, it is essential to understand the relationships
between the different entities and how these relationships evolve over time. In
this paper, we introduce the time-varying graphical lasso (TVGL), a method of
inferring time-varying networks from raw time series data. We cast the problem
in terms of estimating a sparse time-varying inverse covariance matrix, which
reveals a dynamic network of interdependencies between the entities. Since
dynamic network inference is a computationally expensive task, we derive a
scalable message-passing algorithm based on the Alternating Direction Method of
Multipliers (ADMM) to solve this problem in an efficient way. We also discuss
several extensions, including a streaming algorithm to update the model and
incorporate new observations in real time. Finally, we evaluate our TVGL
algorithm on both real and synthetic datasets, obtaining interpretable results
and outperforming state-of-the-art baselines in terms of both accuracy and
scalability.
| David Hallac, Youngsuk Park, Stephen Boyd, Jure Leskovec | null | 1703.01958 | null | null |
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