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12,501 | WHInter: A Working set algorithm for High-dimensional sparse second
order Interaction models | cs.LG | Learning sparse linear models with two-way interactions is desirable in many
application domains such as genomics. l1-regularised linear models are popular
to estimate sparse models, yet standard implementations fail to address
specifically the quadratic explosion of candidate two-way interactions in high
dimensions, a... | computer science |
12,502 | Mining Sub-Interval Relationships In Time Series Data | stat.ML | Time-series data is being increasingly collected and stud- ied in several
areas such as neuroscience, climate science, transportation, and social media.
Discovery of complex patterns of relationships between individual time-series,
using data-driven approaches can improve our understanding of real-world
systems. While ... | computer science |
12,503 | Information-theoretic Limits for Community Detection in Network Models | cs.LG | We analyze the information-theoretic limits for the recovery of node labels
in several network models, including the stochastic block model, as well as the
latent space model. For the stochastic block model, the non-recoverability
condition depends on the probabilities of having edges inside a community, and
between di... | computer science |
12,504 | Interaction Matters: A Note on Non-asymptotic Local Convergence of
Generative Adversarial Networks | stat.ML | Motivated by the pursuit of a systematic computational and algorithmic
understanding of Generative Adversarial Networks (GANs), we present a simple
yet unified non-asymptotic local convergence theory for smooth two-player
games, which subsumes several discrete-time gradient-based saddle point
dynamics. The analysis rev... | computer science |
12,505 | A Likelihood-Free Inference Framework for Population Genetic Data using
Exchangeable Neural Networks | cs.LG | Inference for population genetics models is hindered by computationally
intractable likelihoods. While this issue is tackled by likelihood-free
methods, these approaches typically rely on hand-crafted summary statistics of
the data. In complex settings, designing and selecting suitable summary
statistics is problematic... | computer science |
12,506 | Learning to Race through Coordinate Descent Bayesian Optimisation | cs.RO | In the automation of many kinds of processes, the observable outcome can
often be described as the combined effect of an entire sequence of actions, or
controls, applied throughout its execution. In these cases, strategies to
optimise control policies for individual stages of the process might not be
applicable, and in... | computer science |
12,507 | CREPE: A Convolutional Representation for Pitch Estimation | eess.AS | The task of estimating the fundamental frequency of a monophonic sound
recording, also known as pitch tracking, is fundamental to audio processing
with multiple applications in speech processing and music information
retrieval. To date, the best performing techniques, such as the pYIN algorithm,
are based on a combinat... | computer science |
12,508 | Nonconvex Matrix Factorization from Rank-One Measurements | cs.IT | We consider the problem of recovering low-rank matrices from random rank-one
measurements, which spans numerous applications including covariance sketching,
phase retrieval, quantum state tomography, and learning shallow polynomial
neural networks, among others. Our approach is to directly estimate the
low-rank factor ... | computer science |
12,509 | Unsupervised vehicle recognition using incremental reseeding of acoustic
signatures | stat.ML | Vehicle recognition and classification have broad applications, ranging from
traffic flow management to military target identification. We demonstrate an
unsupervised method for automated identification of moving vehicles from
roadside audio sensors. Using a short-time Fourier transform to decompose audio
signals, we t... | computer science |
12,510 | Nonparametric Estimation of Low Rank Matrix Valued Function | stat.ML | Let $A:[0,1]\rightarrow\mathbb{H}_m$ (the space of Hermitian matrices) be a
matrix valued function which is low rank with entries in H\"{o}lder class
$\Sigma(\beta,L)$. The goal of this paper is to study statistical estimation of
$A$ based on the regression model $\mathbb{E}(Y_j|\tau_j,X_j) = \langle
A(\tau_j), X_j \ra... | computer science |
12,511 | Black-Box Reductions for Parameter-free Online Learning in Banach Spaces | cs.LG | We introduce several new black-box reductions that significantly improve the
design of adaptive and parameter-free online learning algorithms by simplifying
analysis, improving regret guarantees, and sometimes even improving runtime. We
reduce parameter-free online learning to online exp-concave optimization, we
reduce... | computer science |
12,512 | Optimizing Spectral Sums using Randomized Chebyshev Expansions | cs.LG | The trace of matrix functions, often called spectral sums, e.g., rank,
log-determinant and nuclear norm, appear in many machine learning tasks.
However, optimizing or computing such (parameterized) spectral sums typically
involves the matrix decomposition at the cost cubic in the matrix dimension,
which is expensive fo... | computer science |
12,513 | Node Centralities and Classification Performance for Characterizing Node
Embedding Algorithms | cs.LG | Embedding graph nodes into a vector space can allow the use of machine
learning to e.g. predict node classes, but the study of node embedding
algorithms is immature compared to the natural language processing field
because of a diverse nature of graphs. We examine the performance of node
embedding algorithms with respe... | computer science |
12,514 | Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys | math.OC | Neural networks provide a rich class of high-dimensional, non-convex
optimization problems. Despite their non-convexity, gradient-descent methods
often successfully optimize these models. This has motivated a recent spur in
research attempting to characterize properties of their loss surface that may
be responsible for... | computer science |
12,515 | HybridSVD: When Collaborative Information is Not Enough | cs.LG | We propose a hybrid algorithm for top-$n$ recommendation task that allows to
incorporate both user and item side information within the standard
collaborative filtering approach. The algorithm extends PureSVD -- one of the
state-of-the-art latent factor models -- by exploiting a generalized
formulation of the singular ... | computer science |
12,516 | Local Optimality and Generalization Guarantees for the Langevin
Algorithm via Empirical Metastability | cs.LG | We study the detailed path-wise behavior of the discrete-time Langevin
algorithm for non-convex Empirical Risk Minimization (ERM) through the lens of
metastability, adopting some techniques from Berglund and Gentz.
For a particular local optimum of the empirical risk, with an arbitrary
initialization, we show that, w... | computer science |
12,517 | Recommendations with Negative Feedback via Pairwise Deep Reinforcement
Learning | cs.IR | Recommender systems play a crucial role in mitigating the problem of
information overload by suggesting users' personalized items or services. The
vast majority of traditional recommender systems consider the recommendation
procedure as a static process and make recommendations following a fixed
strategy. In this paper... | computer science |
12,518 | Differentially Private Generative Adversarial Network | cs.LG | Generative Adversarial Network (GAN) and its variants have recently attracted
intensive research interests due to their elegant theoretical foundation and
excellent empirical performance as generative models. These tools provide a
promising direction in the studies where data availability is limited. One
common issue i... | computer science |
12,519 | Generalization Error Bounds with Probabilistic Guarantee for SGD in
Nonconvex Optimization | stat.ML | The success of deep learning has led to a rising interest in the
generalization property of the stochastic gradient descent (SGD) method, and
stability is one popular approach to study it. Existing works based on
stability have studied nonconvex loss functions, but only considered the
generalization error of the SGD in... | computer science |
12,520 | Comparison Based Learning from Weak Oracles | cs.LG | There is increasing interest in learning algorithms that involve interaction
between human and machine. Comparison-based queries are among the most natural
ways to get feedback from humans. A challenge in designing comparison-based
interactive learning algorithms is coping with noisy answers. The most common
fix is to ... | computer science |
12,521 | Actively Avoiding Nonsense in Generative Models | cs.LG | A generative model may generate utter nonsense when it is fit to maximize the
likelihood of observed data. This happens due to "model error," i.e., when the
true data generating distribution does not fit within the class of generative
models being learned. To address this, we propose a model of active
distribution lear... | computer science |
12,522 | Steering Social Activity: A Stochastic Optimal Control Point Of View | cs.SI | User engagement in online social networking depends critically on the level
of social activity in the corresponding platform--the number of online actions,
such as posts, shares or replies, taken by their users. Can we design
data-driven algorithms to increase social activity? At a user level, such
algorithms may incre... | computer science |
12,523 | On the Connection Between Learning Two-Layers Neural Networks and Tensor
Decomposition | cs.LG | We establish connections between the problem of learning a two-layers neural
network with good generalization error and tensor decomposition. We consider a
model with input $\boldsymbol x \in \mathbb R^d$, $r$ hidden units with weights
$\{\boldsymbol w_i\}_{1\le i \le r}$ and output $y\in \mathbb R$, i.e.,
$y=\sum_{i=1... | computer science |
12,524 | Sample Complexity of Stochastic Variance-Reduced Cubic Regularization
for Nonconvex Optimization | math.OC | The popular cubic regularization (CR) method converges with first- and
second-order optimality guarantee for nonconvex optimization, but encounters a
high sample complexity issue for solving large-scale problems. Various
sub-sampling variants of CR have been proposed to improve the sample
complexity.In this paper, we p... | computer science |
12,525 | 3LC: Lightweight and Effective Traffic Compression for Distributed
Machine Learning | cs.LG | The performance and efficiency of distributed machine learning (ML) depends
significantly on how long it takes for nodes to exchange state changes.
Overly-aggressive attempts to reduce communication often sacrifice final model
accuracy and necessitate additional ML techniques to compensate for this loss,
limiting their... | computer science |
12,526 | Direct Learning to Rank and Rerank | stat.ML | Learning-to-rank techniques have proven to be extremely useful for
prioritization problems, where we rank items in order of their estimated
probabilities, and dedicate our limited resources to the top-ranked items. This
work exposes a serious problem with the state of learning-to-rank algorithms,
which is that they are... | computer science |
12,527 | Spectrally approximating large graphs with smaller graphs | cs.LG | How does coarsening affect the spectrum of a general graph? We provide
conditions such that the principal eigenvalues and eigenspaces of a coarsened
and original graph Laplacian matrices are close. The achieved approximation is
shown to depend on standard graph-theoretic properties, such as the degree and
eigenvalue di... | computer science |
12,528 | Adversarial classification: An adversarial risk analysis approach | stat.ML | Classification problems in security settings are usually contemplated as
confrontations in which one or more adversaries try to fool a classifier to
obtain a benefit. Most approaches to such adversarial classification problems
have focused on game theoretical ideas with strong underlying common knowledge
assumptions, w... | computer science |
12,529 | Continual Lifelong Learning with Neural Networks: A Review | cs.LG | Humans and animals have the ability to continually acquire and fine-tune
knowledge throughout their lifespan. This ability is mediated by a rich set of
neurocognitive functions that together contribute to the early development and
experience-driven specialization of our sensorimotor skills. Consequently, the
ability to... | computer science |
12,530 | Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests
for Goodness of Fit | stat.ML | We characterize the asymptotic performance of nonparametric goodness of fit
testing, otherwise known as the universal hypothesis testing that dates back to
Hoeffding (1965). The exponential decay rate of the type-II error probability
is used as the asymptotic performance metric, hence an optimal test achieves
the maxim... | computer science |
12,531 | Entropy Rate Estimation for Markov Chains with Large State Space | cs.LG | Estimating the entropy based on data is one of the prototypical problems in
distribution property testing and estimation. For estimating the Shannon
entropy of a distribution on $S$ elements with independent samples,
[Paninski2004] showed that the sample complexity is sublinear in $S$, and
[Valiant--Valiant2011] showed... | computer science |
12,532 | The Hidden Vulnerability of Distributed Learning in Byzantium | stat.ML | While machine learning is going through an era of celebrated success,
concerns have been raised about the vulnerability of its backbone: stochastic
gradient descent (SGD). Recent approaches have been proposed to ensure the
robustness of distributed SGD against adversarial (Byzantine) workers sending
poisoned gradients ... | computer science |
12,533 | Asynchronous Byzantine Machine Learning | stat.ML | Asynchronous distributed machine learning solutions have proven very
effective so far, but always assuming perfectly functioning workers. In
practice, some of the workers can however exhibit Byzantine behavior, caused by
hardware failures, software bugs, corrupt data, or even malicious attacks. We
introduce \emph{Karda... | computer science |
12,534 | The State of the Art in Integrating Machine Learning into Visual
Analytics | stat.ML | Visual analytics systems combine machine learning or other analytic
techniques with interactive data visualization to promote sensemaking and
analytical reasoning. It is through such techniques that people can make sense
of large, complex data. While progress has been made, the tactful combination
of machine learning a... | computer science |
12,535 | VBALD - Variational Bayesian Approximation of Log Determinants | cs.LG | Evaluating the log determinant of a positive definite matrix is ubiquitous in
machine learning. Applications thereof range from Gaussian processes,
minimum-volume ellipsoids, metric learning, kernel learning, Bayesian neural
networks, Determinental Point Processes, Markov random fields to partition
functions of discret... | computer science |
12,536 | Solving Approximate Wasserstein GANs to Stationarity | cs.LG | Generative Adversarial Networks (GANs) are one of the most practical
strategies to learn data distributions. A popular GAN formulation is based on
the use of Wasserstein distance as a metric between probability distributions.
Unfortunately, minimizing the Wasserstein distance between the data
distribution and the gener... | computer science |
12,537 | Deep learning algorithm for data-driven simulation of noisy dynamical
system | cs.LG | We present a deep learning model, DE-LSTM, for the simulation of a stochastic
process with underlying nonlinear dynamics. The deep learning model aims to
approximate the probability density function of a stochastic process via
numerical discretization and the underlying nonlinear dynamics is modeled by
the Long Short-T... | computer science |
12,538 | Learning Without Mixing: Towards A Sharp Analysis of Linear System
Identification | cs.LG | We prove that the ordinary least-squares (OLS) estimator attains nearly
minimax optimal performance for the identification of linear dynamical systems
from a single observed trajectory. Our upper bound relies on a generalization
of Mendelson's small-ball method to dependent data, eschewing the use of
standard mixing-ti... | computer science |
12,539 | Exponentially Consistent Kernel Two-Sample Tests | stat.ML | Given two sets of independent samples from unknown distributions $P$ and $Q$,
a two-sample test decides whether to reject the null hypothesis that $P=Q$.
Recent attention has focused on kernel two-sample tests as the test statistics
are easy to compute, converge fast, and have low bias with their finite sample
estimate... | computer science |
12,540 | Optimized Algorithms to Sample Determinantal Point Processes | stat.CO | In this technical report, we discuss several sampling algorithms for
Determinantal Point Processes (DPP). DPPs have recently gained a broad interest
in the machine learning and statistics literature as random point processes
with negative correlation, i.e., ones that can generate a "diverse" sample from
a set of items.... | computer science |
12,541 | Verifying Controllers Against Adversarial Examples with Bayesian
Optimization | cs.SY | Recent successes in reinforcement learning have lead to the development of
complex controllers for real-world robots. As these robots are deployed in
safety-critical applications and interact with humans, it becomes critical to
ensure safety in order to avoid causing harm. A first step in this direction is
to test the ... | computer science |
12,542 | N-GCN: Multi-scale Graph Convolution for Semi-supervised Node
Classification | cs.LG | Graph Convolutional Networks (GCNs) have shown significant improvements in
semi-supervised learning on graph-structured data. Concurrently, unsupervised
learning of graph embeddings has benefited from the information contained in
random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which
marries th... | computer science |
12,543 | Scalable Private Learning with PATE | stat.ML | The rapid adoption of machine learning has increased concerns about the
privacy implications of machine learning models trained on sensitive data, such
as medical records or other personal information. To address those concerns,
one promising approach is Private Aggregation of Teacher Ensembles, or PATE,
which transfer... | computer science |
12,544 | Averaging Stochastic Gradient Descent on Riemannian Manifolds | cs.LG | We consider the minimization of a function defined on a Riemannian manifold
$\mathcal{M}$ accessible only through unbiased estimates of its gradients. We
develop a geometric framework to transform a sequence of slowly converging
iterates generated from stochastic gradient descent (SGD) on $\mathcal{M}$ to
an averaged i... | computer science |
12,545 | AI4AI: Quantitative Methods for Classifying Host Species from Avian
Influenza DNA Sequence | cs.LG | Avian Influenza breakouts cause millions of dollars in damage each year
globally, especially in Asian countries such as China and South Korea. The
impact magnitude of a breakout directly correlates to time required to fully
understand the influenza virus, particularly the interspecies pathogenicity.
The procedure requi... | computer science |
12,546 | Dimension-free Information Concentration via Exp-Concavity | cs.LG | Information concentration of probability measures have important implications
in learning theory. Recently, it is discovered that the information content of
a log-concave distribution concentrates around their differential entropy,
albeit with an unpleasant dependence on the ambient dimension. In this work, we
prove th... | computer science |
12,547 | Autoencoder based image compression: can the learning be quantization
independent? | eess.IV | This paper explores the problem of learning transforms for image compression
via autoencoders. Usually, the rate-distortion performances of image
compression are tuned by varying the quantization step size. In the case of
autoen-coders, this in principle would require learning one transform per
rate-distortion point at... | computer science |
12,548 | Learning Anonymized Representations with Adversarial Neural Networks | stat.ML | Statistical methods protecting sensitive information or the identity of the
data owner have become critical to ensure privacy of individuals as well as of
organizations. This paper investigates anonymization methods based on
representation learning and deep neural networks, and motivated by novel
information theoretica... | computer science |
12,549 | Best Arm Identification for Contaminated Bandits | math.ST | We propose the Contaminated Best Arm Identification variant of the
Multi-Armed Bandit problem, in which every arm pull has some probability
$\varepsilon$ of generating a sample from an arbitrary \emph{contamination}
distribution instead of the \emph{true} underlying distribution. We would still
like to guarantee that w... | computer science |
12,550 | Human Perceptions of Fairness in Algorithmic Decision Making: A Case
Study of Criminal Risk Prediction | stat.ML | As algorithms are increasingly used to make important decisions that affect
human lives, ranging from social benefit assignment to predicting risk of
criminal recidivism, concerns have been raised about the fairness of
algorithmic decision making. Most prior works on algorithmic fairness
normatively prescribe how fair ... | computer science |
12,551 | Shampoo: Preconditioned Stochastic Tensor Optimization | cs.LG | Preconditioned gradient methods are among the most general and powerful tools
in optimization. However, preconditioning requires storing and manipulating
prohibitively large matrices. We describe and analyze a new structure-aware
preconditioning algorithm, called Shampoo, for stochastic optimization over
tensor spaces.... | computer science |
12,552 | Near-Linear Time Local Polynomial Nonparametric Estimation | stat.CO | Local polynomial regression (Fan & Gijbels, 1996) is an important class of
methods for nonparametric density estimation and regression problems. However,
straightforward implementation of local polynomial regression has quadratic
time complexity which hinders its applicability in large-scale data analysis.
In this pape... | computer science |
12,553 | Understanding and Enhancing the Transferability of Adversarial Examples | stat.ML | State-of-the-art deep neural networks are known to be vulnerable to
adversarial examples, formed by applying small but malicious perturbations to
the original inputs. Moreover, the perturbations can \textit{transfer across
models}: adversarial examples generated for a specific model will often mislead
other unseen mode... | computer science |
12,554 | Bioinformatics and Medicine in the Era of Deep Learning | cs.LG | Many of the current scientific advances in the life sciences have their
origin in the intensive use of data for knowledge discovery. In no area this is
so clear as in bioinformatics, led by technological breakthroughs in data
acquisition technologies. It has been argued that bioinformatics could quickly
become the fiel... | computer science |
12,555 | Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient
Optimization | stat.ML | In this paper, we propose a novel sufficient decrease technique for
stochastic variance reduced gradient descent methods such as SVRG and SAGA. In
order to make sufficient decrease for stochastic optimization, we design a new
sufficient decrease criterion, which yields sufficient decrease versions of
stochastic varianc... | computer science |
12,556 | Deep Reinforcement Learning for Vision-Based Robotic Grasping: A
Simulated Comparative Evaluation of Off-Policy Methods | cs.RO | In this paper, we explore deep reinforcement learning algorithms for
vision-based robotic grasping. Model-free deep reinforcement learning (RL) has
been successfully applied to a range of challenging environments, but the
proliferation of algorithms makes it difficult to discern which particular
approach would be best ... | computer science |
12,557 | Solving for high dimensional committor functions using artificial neural
networks | cs.LG | In this note we propose a method based on artificial neural network to study
the transition between states governed by stochastic processes. In particular,
we aim for numerical schemes for the committor function, the central object of
transition path theory, which satisfies a high-dimensional Fokker-Planck
equation. By... | computer science |
12,558 | Stochastic Dynamic Programming Heuristics for Influence
Maximization-Revenue Optimization | stat.ML | The well-known Influence Maximization (IM) problem has been actively studied
by researchers over the past decade, with emphasis on marketing and social
networks. Existing research have obtained solutions to the IM problem by
obtaining the influence spread and utilizing the property of submodularity.
This paper is based... | computer science |
12,559 | Maximum likelihood estimation of a finite mixture of logistic regression
models in a continuous data stream | cs.LG | In marketing we are often confronted with a continuous stream of responses to
marketing messages. Such streaming data provide invaluable information
regarding message effectiveness and segmentation. However, streaming data are
hard to analyze using conventional methods: their high volume and the fact that
they are cont... | computer science |
12,560 | A Variational Inequality Perspective on Generative Adversarial Nets | cs.LG | Stability has been a recurrent issue in training generative adversarial
networks (GANs). One common way to tackle this issue has been to propose new
formulations of the GAN objective. Yet, surprisingly few studies have looked at
optimization methods specifically designed for this adversarial training. In
this work, we ... | computer science |
12,561 | Learning by Playing - Solving Sparse Reward Tasks from Scratch | cs.LG | We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in
the context of Reinforcement Learning (RL). SAC-X enables learning of complex
behaviors - from scratch - in the presence of multiple sparse reward signals.
To this end, the agent is equipped with a set of general auxiliary tasks, that
it attempt... | computer science |
12,562 | Approximate Inference for Constructing Astronomical Catalogs from Images | stat.AP | We present a new, fully generative model for constructing astronomical
catalogs from optical telescope image sets. Each pixel intensity is treated as
a Poisson random variable with a rate parameter that depends on the latent
properties of stars and galaxies. These latent properties are themselves
random, with scientifi... | computer science |
12,563 | SQL-Rank: A Listwise Approach to Collaborative Ranking | stat.ML | In this paper, we propose a listwise approach for constructing user-specific
rankings in recommendation systems in a collaborative fashion. We contrast the
listwise approach to previous pointwise and pairwise approaches, which are
based on treating either each rating or each pairwise comparison as an
independent instan... | computer science |
12,564 | Deep Learning for Causal Inference | econ.EM | In this paper, we propose deep learning techniques for econometrics,
specifically for causal inference and for estimating individual as well as
average treatment effects. The contribution of this paper is twofold: 1. For
generalized neighbor matching to estimate individual and average treatment
effects, we analyze the ... | computer science |
12,565 | Smoothed analysis for low-rank solutions to semidefinite programs in
quadratic penalty form | stat.ML | Semidefinite programs (SDP) are important in learning and combinatorial
optimization with numerous applications. In pursuit of low-rank solutions and
low complexity algorithms, we consider the Burer--Monteiro factorization
approach for solving SDPs. We show that all approximate local optima are global
optima for the pe... | computer science |
12,566 | Block Coordinate Descent for Deep Learning: Unified Convergence
Guarantees | math.OC | Training deep neural networks (DNNs) efficiently is a challenge due to the
associated highly nonconvex optimization. Recently, the efficiency of the block
coordinate descent (BCD) type methods has been empirically illustrated for DNN
training. The main idea of BCD is to decompose the highly composite and
nonconvex DNN ... | computer science |
12,567 | Model-Based Clustering and Classification of Functional Data | stat.ML | The problem of complex data analysis is a central topic of modern statistical
science and learning systems and is becoming of broader interest with the
increasing prevalence of high-dimensional data. The challenge is to develop
statistical models and autonomous algorithms that are able to acquire knowledge
from raw dat... | computer science |
12,568 | Tractable and Scalable Schatten Quasi-Norm Approximations for Rank
Minimization | cs.LG | The Schatten quasi-norm was introduced to bridge the gap between the trace
norm and rank function. However, existing algorithms are too slow or even
impractical for large-scale problems. Motivated by the equivalence relation
between the trace norm and its bilinear spectral penalty, we define two
tractable Schatten norm... | computer science |
12,569 | The Power Mean Laplacian for Multilayer Graph Clustering | stat.ML | Multilayer graphs encode different kind of interactions between the same set
of entities. When one wants to cluster such a multilayer graph, the natural
question arises how one should merge the information different layers. We
introduce in this paper a one-parameter family of matrix power means for
merging the Laplacia... | computer science |
12,570 | Online Feature Ranking for Intrusion Detection Systems | cs.CR | Many current approaches to the design of intrusion detec- tion systems apply
feature selection in a static, non-adaptive fashion. These methods often
neglect the dynamic nature of network data which requires to use adaptive
feature selection techniques. In this paper, we present a simple technique
based on incremental ... | computer science |
12,571 | Static and Dynamic Robust PCA via Low-Rank + Sparse Matrix
Decomposition: A Review | cs.IT | Principal Components Analysis (PCA) is one of the most widely used dimension
reduction techniques. Robust PCA (RPCA) refers to the problem of PCA when the
data may be corrupted by outliers. Recent work by Candes, Wright, Li, and Ma
defined RPCA as a problem of decomposing a given data matrix into the sum of a
low-rank ... | computer science |
12,572 | NetGAN: Generating Graphs via Random Walks | stat.ML | We propose NetGAN - the first implicit generative model for graphs able to
mimic real-world networks. We pose the problem of graph generation as learning
the distribution of biased random walks over the input graph. The proposed
model is based on a stochastic neural network that generates discrete output
samples and is... | computer science |
12,573 | Label Sanitization against Label Flipping Poisoning Attacks | stat.ML | Many machine learning systems rely on data collected in the wild from
untrusted sources, exposing the learning algorithms to data poisoning.
Attackers can inject malicious data in the training dataset to subvert the
learning process, compromising the performance of the algorithm producing
errors in a targeted or an ind... | computer science |
12,574 | Multiresolution Tensor Decomposition for Multiple Spatial Passing
Networks | stat.AP | This article is motivated by soccer positional passing networks collected
across multiple games. We refer to these data as replicated spatial passing
networks---to accurately model such data it is necessary to take into account
the spatial positions of the passer and receiver for each passing event. This
spatial regist... | computer science |
12,575 | Nonnegative Matrix Factorization for Signal and Data Analytics:
Identifiability, Algorithms, and Applications | eess.SP | Nonnegative matrix factorization (NMF) has become a workhorse for signal and
data analytics, triggered by its model parsimony and interpretability. Perhaps
a bit surprisingly, the understanding to its model identifiability---the major
reason behind the interpretability in many applications such as topic mining
and hype... | computer science |
12,576 | Distributed Nonparametric Regression under Communication Constraints | stat.ML | This paper studies the problem of nonparametric estimation of a smooth
function with data distributed across multiple machines. We assume an
independent sample from a white noise model is collected at each machine, and
an estimator of the underlying true function needs to be constructed at a
central machine. We place l... | computer science |
12,577 | A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization
with Nonsmooth Regularization | math.OC | In this paper, we propose a communication- and computation- efficient
distributed optimization algorithm using second- order information for solving
ERM problems with a nonsmooth regularization term. Current second-order and
quasi- Newton methods for this problem either do not work well in the
distributed setting or wo... | computer science |
12,578 | Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates | cs.LG | In large-scale distributed learning, security issues have become increasingly
important. Particularly in a decentralized environment, some computing units
may behave abnormally, or even exhibit Byzantine failures---arbitrary and
potentially adversarial behavior. In this paper, we develop distributed
learning algorithms... | computer science |
12,579 | Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in
MDPs | stat.ML | The problem of reinforcement learning in an unknown and discrete Markov
Decision Process (MDP) under the average-reward criterion is considered, when
the learner interacts with the system in a single stream of observations,
starting from an initial state without any reset. We revisit the minimax lower
bound for that pr... | computer science |
12,580 | Energy-entropy competition and the effectiveness of stochastic gradient
descent in machine learning | cs.LG | Finding parameters that minimise a loss function is at the core of many
machine learning methods. The Stochastic Gradient Descent algorithm is widely
used and delivers state of the art results for many problems. Nonetheless,
Stochastic Gradient Descent typically cannot find the global minimum, thus its
empirical effect... | computer science |
12,581 | An Online Algorithm for Learning Buyer Behavior under Realistic Pricing
Restrictions | stat.ML | We propose a new efficient online algorithm to learn the parameters governing
the purchasing behavior of a utility maximizing buyer, who responds to prices,
in a repeated interaction setting. The key feature of our algorithm is that it
can learn even non-linear buyer utility while working with arbitrary price
constrain... | computer science |
12,582 | Learning Filter Bank Sparsifying Transforms | stat.ML | Data is said to follow the transform (or analysis) sparsity model if it
becomes sparse when acted on by a linear operator called a sparsifying
transform. Several algorithms have been designed to learn such a transform
directly from data, and data-adaptive sparsifying transforms have demonstrated
excellent performance i... | computer science |
12,583 | MIMO Graph Filters for Convolutional Neural Networks | cs.LG | Superior performance and ease of implementation have fostered the adoption of
Convolutional Neural Networks (CNNs) for a wide array of inference and
reconstruction tasks. CNNs implement three basic blocks: convolution, pooling
and pointwise nonlinearity. Since the two first operations are well-defined
only on regular-s... | computer science |
12,584 | Dimensionality Reduction for Stationary Time Series via Stochastic
Nonconvex Optimization | cs.LG | Stochastic optimization naturally arises in machine learning. Efficient
algorithms with provable guarantees, however, are still largely missing, when
the objective function is nonconvex and the data points are dependent. This
paper studies this fundamental challenge through a streaming PCA problem for
stationary time s... | computer science |
12,585 | Visualizing Convolutional Neural Network Protein-Ligand Scoring | stat.ML | Protein-ligand scoring is an important step in a structure-based drug design
pipeline. Selecting a correct binding pose and predicting the binding affinity
of a protein-ligand complex enables effective virtual screening. Machine
learning techniques can make use of the increasing amounts of structural data
that are beco... | computer science |
12,586 | Masked Conditional Neural Networks for Audio Classification | stat.ML | We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL
Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes
into consideration the temporal nature of the sound signal and the MCLNN
extends upon the CLNN through a binary mask to preserve the spatial locality of
the feature... | computer science |
12,587 | Graph Learning from Filtered Signals: Graph System and Diffusion Kernel
Identification | cs.LG | This paper introduces a novel graph signal processing framework for building
graph-based models from classes of filtered signals. In our framework,
graph-based modeling is formulated as a graph system identification problem,
where the goal is to learn a weighted graph (a graph Laplacian matrix) and a
graph-based filter... | computer science |
12,588 | Revisiting differentially private linear regression: optimal and
adaptive prediction & estimation in unbounded domain | stat.ML | We revisit the problem of linear regression under a differential privacy
constraint. By consolidating existing pieces in the literature, we clarify the
correct dependence of the feature, label and coefficient domain in the
optimization error and estimation error, hence revealing the delicate price of
differential priva... | computer science |
12,589 | Generating Differentially Private Datasets Using GANs | cs.LG | In this paper, we present a technique for generating artificial datasets that
retain statistical properties of the real data while providing differential
privacy guarantees with respect to this data. We include a Gaussian noise layer
in the discriminator of a generative adversarial network to make the output and
the gr... | computer science |
12,590 | Ripple Network: Propagating User Preferences on the Knowledge Graph for
Recommender Systems | cs.IR | To address the sparsity and cold start problem of collaborative filtering,
researchers usually make use of side information, such as social networks or
item attributes, to improve recommendation performance. This paper considers
the knowledge graph as the source of side information. To address the
limitations of existi... | computer science |
12,591 | Explaining Black-box Android Malware Detection | cs.LG | Machine-learning models have been recently used for detecting malicious
Android applications, reporting impressive performances on benchmark datasets,
even when trained only on features statically extracted from the application,
such as system calls and permissions. However, recent findings have highlighted
the fragili... | computer science |
12,592 | Standing Wave Decomposition Gaussian Process | stat.ML | We propose a Standing Wave Decomposition (SWD) approximation to Gaussian
Process regression (GP). GP involves a costly matrix inversion operation, which
limits applicability to large data analysis. For an input space that can be
approximated by a grid and when correlations among data are short-ranged, the
kernel matrix... | computer science |
12,593 | Combating Adversarial Attacks Using Sparse Representations | stat.ML | It is by now well-known that small adversarial perturbations can induce
classification errors in deep neural networks (DNNs). In this paper, we make
the case that sparse representations of the input data are a crucial tool for
combating such attacks. For linear classifiers, we show that a sparsifying
front end is prova... | computer science |
12,594 | A pathway-based kernel boosting method for sample classification using
genomic data | stat.ML | The analysis of cancer genomic data has long suffered "the curse of
dimensionality". Sample sizes for most cancer genomic studies are a few
hundreds at most while there are tens of thousands of genomic features studied.
Various methods have been proposed to leverage prior biological knowledge, such
as pathways, to more... | computer science |
12,595 | Detecting Nonlinear Causality in Multivariate Time Series with Sparse
Additive Models | stat.ML | We propose a nonparametric method for detecting nonlinear causal relationship
within a set of multidimensional discrete time series, by using sparse additive
models (SpAMs). We show that, when the input to the SpAM is a $\beta$-mixing
time series, the model can be fitted by first approximating each unknown
function wit... | computer science |
12,596 | BEBP: An Poisoning Method Against Machine Learning Based IDSs | cs.LG | In big data era, machine learning is one of fundamental techniques in
intrusion detection systems (IDSs). However, practical IDSs generally update
their decision module by feeding new data then retraining learning models in a
periodical way. Hence, some attacks that comprise the data for training or
testing classifiers... | computer science |
12,597 | Link prediction for egocentrically sampled networks | stat.CO | Link prediction in networks is typically accomplished by estimating or
ranking the probabilities of edges for all pairs of nodes. In practice,
especially for social networks, the data are often collected by egocentric
sampling, which means selecting a subset of nodes and recording all of their
edges. This sampling mech... | computer science |
12,598 | Extreme Learning Machine for Graph Signal Processing | stat.ML | In this article, we improve extreme learning machines for regression tasks
using a graph signal processing based regularization. We assume that the target
signal for prediction or regression is a graph signal. With this assumption, we
use the regularization to enforce that the output of an extreme learning
machine is s... | computer science |
12,599 | High Throughput Synchronous Distributed Stochastic Gradient Descent | cs.DC | We introduce a new, high-throughput, synchronous, distributed, data-parallel,
stochastic-gradient-descent learning algorithm. This algorithm uses amortized
inference in a compute-cluster-specific, deep, generative, dynamical model to
perform joint posterior predictive inference of the mini-batch gradient
computation ti... | computer science |
12,600 | Leveraging Crowdsourcing Data For Deep Active Learning - An Application:
Learning Intents in Alexa | cs.LG | This paper presents a generic Bayesian framework that enables any deep
learning model to actively learn from targeted crowds. Our framework inherits
from recent advances in Bayesian deep learning, and extends existing work by
considering the targeted crowdsourcing approach, where multiple annotators with
unknown expert... | computer science |
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