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11,801 | Modelling Competitive Sports: Bradley-Terry-Élő Models for
Supervised and On-Line Learning of Paired Competition Outcomes | stat.ML | Prediction and modelling of competitive sports outcomes has received much
recent attention, especially from the Bayesian statistics and machine learning
communities. In the real world setting of outcome prediction, the seminal
\'{E}l\H{o} update still remains, after more than 50 years, a valuable baseline
which is diff... | computer science |
11,802 | Deep Recurrent Neural Network for Protein Function Prediction from
Sequence | cs.LG | As high-throughput biological sequencing becomes faster and cheaper, the need
to extract useful information from sequencing becomes ever more paramount,
often limited by low-throughput experimental characterizations. For proteins,
accurate prediction of their functions directly from their primary amino-acid
sequences h... | computer science |
11,803 | Bayesian Learning of Consumer Preferences for Residential Demand
Response | cs.LG | In coming years residential consumers will face real-time electricity tariffs
with energy prices varying day to day, and effective energy saving will require
automation - a recommender system, which learns consumer's preferences from her
actions. A consumer chooses a scenario of home appliance use to balance her
comfor... | computer science |
11,804 | Learning the distribution with largest mean: two bandit frameworks | cs.LG | Over the past few years, the multi-armed bandit model has become increasingly
popular in the machine learning community, partly because of applications
including online content optimization. This paper reviews two different
sequential learning tasks that have been considered in the bandit literature ;
they can be formu... | computer science |
11,805 | Representation of big data by dimension reduction | cs.IT | Suppose the data consist of a set $S$ of points $x_j, 1 \leq j \leq J$,
distributed in a bounded domain $D \subset R^N$, where $N$ and $J$ are large
numbers. In this paper an algorithm is proposed for checking whether there
exists a manifold $\mathbb{M}$ of low dimension near which many of the points
of $S$ lie and fin... | computer science |
11,806 | On SGD's Failure in Practice: Characterizing and Overcoming Stalling | stat.ML | Stochastic Gradient Descent (SGD) is widely used in machine learning problems
to efficiently perform empirical risk minimization, yet, in practice, SGD is
known to stall before reaching the actual minimizer of the empirical risk. SGD
stalling has often been attributed to its sensitivity to the conditioning of
the probl... | computer science |
11,807 | Generative Adversarial Networks recover features in astrophysical images
of galaxies beyond the deconvolution limit | cs.LG | Observations of astrophysical objects such as galaxies are limited by various
sources of random and systematic noise from the sky background, the optical
system of the telescope and the detector used to record the data. Conventional
deconvolution techniques are limited in their ability to recover features in
imaging da... | computer science |
11,808 | Natasha: Faster Non-Convex Stochastic Optimization Via Strongly
Non-Convex Parameter | math.OC | Given a nonconvex function $f(x)$ that is an average of $n$ smooth functions,
we design stochastic first-order methods to find its approximate stationary
points. The performance of our new methods depend on the smallest (negative)
eigenvalue $-\sigma$ of the Hessian. This parameter $\sigma$ captures how
strongly noncon... | computer science |
11,809 | Search Intelligence: Deep Learning For Dominant Category Prediction | cs.IR | Deep Neural Networks, and specifically fully-connected convolutional neural
networks are achieving remarkable results across a wide variety of domains.
They have been trained to achieve state-of-the-art performance when applied to
problems such as speech recognition, image classification, natural language
processing an... | computer science |
11,810 | Integration of Machine Learning Techniques to Evaluate Dynamic Customer
Segmentation Analysis for Mobile Customers | cs.CY | The telecommunications industry is highly competitive, which means that the
mobile providers need a business intelligence model that can be used to achieve
an optimal level of churners, as well as a minimal level of cost in marketing
activities. Machine learning applications can be used to provide guidance on
marketing... | computer science |
11,811 | Matrix Completion from $O(n)$ Samples in Linear Time | stat.ML | We consider the problem of reconstructing a rank-$k$ $n \times n$ matrix $M$
from a sampling of its entries. Under a certain incoherence assumption on $M$
and for the case when both the rank and the condition number of $M$ are
bounded, it was shown in \cite{CandesRecht2009, CandesTao2010, keshavan2010,
Recht2011, Jain2... | computer science |
11,812 | Adversarial Attacks on Neural Network Policies | cs.LG | Machine learning classifiers are known to be vulnerable to inputs maliciously
constructed by adversaries to force misclassification. Such adversarial
examples have been extensively studied in the context of computer vision
applications. In this work, we show adversarial attacks are also effective when
targeting neural ... | computer science |
11,813 | Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier | cs.LG | We study the problem of ranking a set of items from nonactively chosen
pairwise preferences where each item has feature information with it. We
propose and characterize a very broad class of preference matrices giving rise
to the Feature Low Rank (FLR) model, which subsumes several models ranging from
the classic Bradl... | computer science |
11,814 | Rate Optimal Estimation and Confidence Intervals for High-dimensional
Regression with Missing Covariates | stat.ML | Although a majority of the theoretical literature in high-dimensional
statistics has focused on settings which involve fully-observed data, settings
with missing values and corruptions are common in practice. We consider the
problems of estimation and of constructing component-wise confidence intervals
in a sparse high... | computer science |
11,815 | A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse
Gaussian Graphical Models | stat.ML | Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many
related tasks (large $K$) under a high-dimensional (large $p$) situation is an
important task. Most previous studies for the joint estimation of multiple
sGGMs rely on penalized log-likelihood estimators that involve expensive and
difficult n... | computer science |
11,816 | Fixing an error in Caponnetto and de Vito (2007) | stat.ML | The seminal paper of Caponnetto and de Vito (2007) provides minimax-optimal
rates for kernel ridge regression in a very general setting. Its proof,
however, contains an error in its bound on the effective dimensionality. In
this note, we explain the mistake, provide a correct bound, and show that the
main theorem remai... | computer science |
11,817 | Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection | cs.LG | In the Best-$k$-Arm problem, we are given $n$ stochastic bandit arms, each
associated with an unknown reward distribution. We are required to identify the
$k$ arms with the largest means by taking as few samples as possible. In this
paper, we make progress towards a complete characterization of the
instance-wise sample... | computer science |
11,818 | Non-convex learning via Stochastic Gradient Langevin Dynamics: a
nonasymptotic analysis | cs.LG | Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of
Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is
added to an unbiased estimate of the gradient at each iteration. This modest
change allows SGLD to escape local minima and suffices to guarantee asymptotic
convergence to g... | computer science |
11,819 | Mutual Kernel Matrix Completion | cs.LG | With the huge influx of various data nowadays, extracting knowledge from them
has become an interesting but tedious task among data scientists, particularly
when the data come in heterogeneous form and have missing information. Many
data completion techniques had been introduced, especially in the advent of
kernel meth... | computer science |
11,820 | Gaussian-Dirichlet Posterior Dominance in Sequential Learning | stat.ML | We consider the problem of sequential learning from categorical observations
bounded in [0,1]. We establish an ordering between the Dirichlet posterior over
categorical outcomes and a Gaussian posterior under observations with N(0,1)
noise. We establish that, conditioned upon identical data with at least two
observatio... | computer science |
11,821 | Sketched Ridge Regression: Optimization Perspective, Statistical
Perspective, and Model Averaging | stat.ML | We address the statistical and optimization impacts of using classical sketch
versus Hessian sketch to solve approximately the Matrix Ridge Regression (MRR)
problem. Prior research has considered the effects of classical sketch on least
squares regression (LSR), a strictly simpler problem. We establish that
classical s... | computer science |
11,822 | Latent Laplacian Maximum Entropy Discrimination for Detection of
High-Utility Anomalies | stat.ML | Data-driven anomaly detection methods suffer from the drawback of detecting
all instances that are statistically rare, irrespective of whether the detected
instances have real-world significance or not. In this paper, we are interested
in the problem of specifically detecting anomalous instances that are known to
have ... | computer science |
11,823 | RIPML: A Restricted Isometry Property based Approach to Multilabel
Learning | cs.IR | The multilabel learning problem with large number of labels, features, and
data-points has generated a tremendous interest recently. A recurring theme of
these problems is that only a few labels are active in any given datapoint as
compared to the total number of labels. However, only a small number of
existing work ta... | computer science |
11,824 | Completing a joint PMF from projections: a low-rank coupled tensor
factorization approach | cs.LG | There has recently been considerable interest in completing a low-rank matrix
or tensor given only a small fraction (or few linear combinations) of its
entries. Related approaches have found considerable success in the area of
recommender systems, under machine learning. From a statistical estimation
point of view, the... | computer science |
11,825 | Maximally Correlated Principal Component Analysis | stat.ML | In the era of big data, reducing data dimensionality is critical in many
areas of science. Widely used Principal Component Analysis (PCA) addresses this
problem by computing a low dimensional data embedding that maximally explain
variance of the data. However, PCA has two major weaknesses. Firstly, it only
considers li... | computer science |
11,826 | Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial
Multi-armed Bandits | cs.LG | Recent work on follow the perturbed leader (FTPL) algorithms for the
adversarial multi-armed bandit problem has highlighted the role of the hazard
rate of the distribution generating the perturbations. Assuming that the hazard
rate is bounded, it is possible to provide regret analyses for a variety of
FTPL algorithms f... | computer science |
11,827 | A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics | cs.LG | We study the Stochastic Gradient Langevin Dynamics (SGLD) algorithm for
non-convex optimization. The algorithm performs stochastic gradient descent,
where in each step it injects appropriately scaled Gaussian noise to the
update. We analyze the algorithm's hitting time to an arbitrary subset of the
parameter space. Two... | computer science |
11,828 | Riemannian stochastic variance reduced gradient | cs.LG | Stochastic variance reduction algorithms have recently become popular for
minimizing the average of a large but finite number of loss functions. In this
paper, we propose a novel Riemannian extension of the Euclidean stochastic
variance reduced gradient algorithm (R-SVRG) to a manifold search space. The
key challenges ... | computer science |
11,829 | Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield
Networks with Arbitrary Priors | cs.LG | Restricted Boltzmann Machines are described by the Gibbs measure of a
bipartite spin glass, which in turn corresponds to the one of a generalised
Hopfield network. This equivalence allows us to characterise the state of these
systems in terms of retrieval capabilities, both at low and high load. We study
the paramagnet... | computer science |
11,830 | On the (Statistical) Detection of Adversarial Examples | cs.CR | Machine Learning (ML) models are applied in a variety of tasks such as
network intrusion detection or Malware classification. Yet, these models are
vulnerable to a class of malicious inputs known as adversarial examples. These
are slightly perturbed inputs that are classified incorrectly by the ML model.
The mitigation... | computer science |
11,831 | Fast rates for online learning in Linearly Solvable Markov Decision
Processes | cs.LG | We study the problem of online learning in a class of Markov decision
processes known as linearly solvable MDPs. In the stationary version of this
problem, a learner interacts with its environment by directly controlling the
state transitions, attempting to balance a fixed state-dependent cost and a
certain smooth cost... | computer science |
11,832 | Memory Matching Networks for Genomic Sequence Classification | cs.LG | When analyzing the genome, researchers have discovered that proteins bind to
DNA based on certain patterns of the DNA sequence known as "motifs". However,
it is difficult to manually construct motifs due to their complexity. Recently,
externally learned memory models have proven to be effective methods for
reasoning ov... | computer science |
11,833 | SIGNet: Scalable Embeddings for Signed Networks | stat.ML | Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distribute... | computer science |
11,834 | On the Power of Truncated SVD for General High-rank Matrix Estimation
Problems | stat.ML | We show that given an estimate $\widehat{A}$ that is close to a general
high-rank positive semi-definite (PSD) matrix $A$ in spectral norm (i.e.,
$\|\widehat{A}-A\|_2 \leq \delta$), the simple truncated SVD of $\widehat{A}$
produces a multiplicative approximation of $A$ in Frobenius norm. This
observation leads to many... | computer science |
11,835 | Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe | cs.LG | We consider the problem of bandit optimization, inspired by stochastic
optimization and online learning problems with bandit feedback. In this
problem, the objective is to minimize a global loss function of all the
actions, not necessarily a cumulative loss. This framework allows us to study a
very general class of pro... | computer science |
11,836 | Distributed Representation of Subgraphs | cs.SI | Network embeddings have become very popular in learning effective feature
representations of networks. Motivated by the recent successes of embeddings in
natural language processing, researchers have tried to find network embeddings
in order to exploit machine learning algorithms for mining tasks like node
classificati... | computer science |
11,837 | When Lempel-Ziv-Welch Meets Machine Learning: A Case Study of
Accelerating Machine Learning using Coding | cs.LG | In this paper we study the use of coding techniques to accelerate machine
learning (ML). Coding techniques, such as prefix codes, have been extensively
studied and used to accelerate low-level data processing primitives such as
scans in a relational database system. However, there is little work on how to
exploit them ... | computer science |
11,838 | On Polynomial Time Methods for Exact Low Rank Tensor Completion | stat.ML | In this paper, we investigate the sample size requirement for exact recovery
of a high order tensor of low rank from a subset of its entries. We show that a
gradient descent algorithm with initial value obtained from a spectral method
can, in particular, reconstruct a ${d\times d\times d}$ tensor of multilinear
ranks $... | computer science |
11,839 | Learning Hawkes Processes from Short Doubly-Censored Event Sequences | cs.LG | Many real-world applications require robust algorithms to learn point
processes based on a type of incomplete data --- the so-called short
doubly-censored (SDC) event sequences. We study this critical problem of
quantitative asynchronous event sequence analysis under the framework of Hawkes
processes by leveraging the ... | computer science |
11,840 | Scalable Inference for Nested Chinese Restaurant Process Topic Models | stat.ML | Nested Chinese Restaurant Process (nCRP) topic models are powerful
nonparametric Bayesian methods to extract a topic hierarchy from a given text
corpus, where the hierarchical structure is automatically determined by the
data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of
nCRP topic models. H... | computer science |
11,841 | A minimax and asymptotically optimal algorithm for stochastic bandits | stat.ML | We propose the kl-UCB ++ algorithm for regret minimization in stochastic
bandit models with exponential families of distributions. We prove that it is
simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower
bound) and minimax optimal. This is the first algorithm proved to enjoy these
two propertie... | computer science |
11,842 | A Goal-Based Movement Model for Continuous Multi-Agent Tasks | cs.LG | Despite increasing attention paid to the need for fast, scalable methods to
analyze next-generation neuroscience data, comparatively little attention has
been paid to the development of similar methods for behavioral analysis. Just
as the volume and complexity of brain data have grown, behavioral paradigms in
systems n... | computer science |
11,843 | A Converse to Banach's Fixed Point Theorem and its CLS Completeness | cs.CC | Banach's fixed point theorem for contraction maps has been widely used to
analyze the convergence of iterative methods in non-convex problems. It is a
common experience, however, that iterative maps fail to be globally contracting
under the natural metric in their domain, making the applicability of Banach's
theorem li... | computer science |
11,844 | Deep Models Under the GAN: Information Leakage from Collaborative Deep
Learning | cs.CR | Deep Learning has recently become hugely popular in machine learning,
providing significant improvements in classification accuracy in the presence
of highly-structured and large databases.
Researchers have also considered privacy implications of deep learning.
Models are typically trained in a centralized manner wit... | computer science |
11,845 | Control of Gene Regulatory Networks with Noisy Measurements and
Uncertain Inputs | cs.LG | This paper is concerned with the problem of stochastic control of gene
regulatory networks (GRNs) observed indirectly through noisy measurements and
with uncertainty in the intervention inputs. The partial observability of the
gene states and uncertainty in the intervention process are accounted for by
modeling GRNs us... | computer science |
11,846 | Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference
Learning | stat.ML | We analyze the problem of learning a single user's preferences in an active
learning setting, sequentially and adaptively querying the user over a finite
time horizon. Learning is conducted via choice-based queries, where the user
selects her preferred option among a small subset of offered alternatives.
These queries ... | computer science |
11,847 | Computationally Efficient Robust Estimation of Sparse Functionals | stat.ML | Many conventional statistical procedures are extremely sensitive to seemingly
minor deviations from modeling assumptions. This problem is exacerbated in
modern high-dimensional settings, where the problem dimension can grow with and
possibly exceed the sample size. We consider the problem of robust estimation
of sparse... | computer science |
11,848 | Efficient coordinate-wise leading eigenvector computation | cs.NA | We develop and analyze efficient "coordinate-wise" methods for finding the
leading eigenvector, where each step involves only a vector-vector product. We
establish global convergence with overall runtime guarantees that are at least
as good as Lanczos's method and dominate it for slowly decaying spectrum. Our
methods a... | computer science |
11,849 | Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs | cs.LG | Deep learning models are often successfully trained using gradient descent,
despite the worst case hardness of the underlying non-convex optimization
problem. The key question is then under what conditions can one prove that
optimization will succeed. Here we provide a strong result of this kind. We
consider a neural n... | computer science |
11,850 | Dropping Convexity for More Efficient and Scalable Online Multiview
Learning | cs.LG | Multiview representation learning is very popular for latent factor analysis.
It naturally arises in many data analysis, machine learning, and information
retrieval applications to model dependent structures among multiple data
sources. For computational convenience, existing approaches usually formulate
the multiview ... | computer science |
11,851 | Algorithmic Chaining and the Role of Partial Feedback in Online
Nonparametric Learning | stat.ML | We investigate contextual online learning with nonparametric (Lipschitz)
comparison classes under different assumptions on losses and feedback
information. For full information feedback and Lipschitz losses, we design the
first explicit algorithm achieving the minimax regret rate (up to log factors).
In a partial feedb... | computer science |
11,852 | Depth Separation for Neural Networks | cs.LG | Let $f:\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}\to\mathbb{S}$ be a function of
the form $f(\mathbf{x},\mathbf{x}') = g(\langle\mathbf{x},\mathbf{x}'\rangle)$
for $g:[-1,1]\to \mathbb{R}$. We give a simple proof that shows that poly-size
depth two neural networks with (exponentially) bounded weights cannot
approximate $f... | computer science |
11,853 | SGD Learns the Conjugate Kernel Class of the Network | cs.LG | We show that the standard stochastic gradient decent (SGD) algorithm is
guaranteed to learn, in polynomial time, a function that is competitive with
the best function in the conjugate kernel space of the network, as defined in
Daniely, Frostig and Singer. The result holds for log-depth networks from a
rich family of ar... | computer science |
11,854 | Can Boltzmann Machines Discover Cluster Updates ? | cs.LG | Boltzmann machines are physics informed generative models with wide
applications in machine learning. They can learn the probability distribution
from an input dataset and generate new samples accordingly. Applying them back
to physics, the Boltzmann machines are ideal recommender systems to accelerate
Monte Carlo simu... | computer science |
11,855 | On architectural choices in deep learning: From network structure to
gradient convergence and parameter estimation | cs.LG | We study mechanisms to characterize how the asymptotic convergence of
backpropagation in deep architectures, in general, is related to the network
structure, and how it may be influenced by other design choices including
activation type, denoising and dropout rate. We seek to analyze whether network
architecture and in... | computer science |
11,856 | Finding Significant Combinations of Continuous Features | stat.ML | We present an efficient feature selection method that can find all
multiplicative combinations of continuous features that are statistically
significantly associated with the class variable, while rigorously correcting
for multiple testing. The key to overcome the combinatorial explosion in the
number of candidates is ... | computer science |
11,857 | Learning rates for classification with Gaussian kernels | cs.LG | This paper aims at refined error analysis for binary classification using
support vector machine (SVM) with Gaussian kernel and convex loss. Our first
result shows that for some loss functions such as the truncated quadratic loss
and quadratic loss, SVM with Gaussian kernel can reach the almost optimal
learning rate, p... | computer science |
11,858 | Hierarchical Implicit Models and Likelihood-Free Variational Inference | stat.ML | Implicit probabilistic models are a flexible class of models defined by a
simulation process for data. They form the basis for theories which encompass
our understanding of the physical world. Despite this fundamental nature, the
use of implicit models remains limited due to challenges in specifying complex
latent stru... | computer science |
11,859 | Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A
Machine Learning Approach | cs.SY | Data-target pairing is an important step towards multi-target localization
for the intelligent operation of unmanned systems. Target localization plays a
crucial role in numerous applications, such as search, and rescue missions,
traffic management and surveillance. The objective of this paper is to present
an innovati... | computer science |
11,860 | SARAH: A Novel Method for Machine Learning Problems Using Stochastic
Recursive Gradient | stat.ML | In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH),
as well as its practical variant SARAH+, as a novel approach to the finite-sum
minimization problems. Different from the vanilla SGD and other modern
stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple
recursive framewor... | computer science |
11,861 | Preserving Differential Privacy Between Features in Distributed
Estimation | stat.ML | 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 t... | computer science |
11,862 | Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for
Regularized Empirical Risk Minimization | math.OC | 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 es... | computer science |
11,863 | MoleculeNet: A Benchmark for Molecular Machine Learning | cs.LG | 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 b... | computer science |
11,864 | In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential
Importance Sampling | cs.LG | 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 s... | computer science |
11,865 | How to Escape Saddle Points Efficiently | cs.LG | 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... | computer science |
11,866 | Differentially Private Bayesian Learning on Distributed Data | stat.ML | 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 entir... | computer science |
11,867 | Generative Poisoning Attack Method Against Neural Networks | cs.CR | 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) h... | computer science |
11,868 | Convex Geometry of the Generalized Matrix-Fractional Function | math.OC | 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 func... | computer science |
11,869 | Recurrent Poisson Factorization for Temporal Recommendation | cs.SI | 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. How... | computer science |
11,870 | Adversarial Generation of Real-time Feedback with Neural Networks for
Simulation-based Training | cs.LG | 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 i... | computer science |
11,871 | Graph sampling with determinantal processes | cs.LG | 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, ... | computer science |
11,872 | Cheshire: An Online Algorithm for Activity Maximization in Social
Networks | stat.ML | User engagement in social networks depends critically on the number of online
actions their users take in the network. Can we design an algorithm that finds
when to incentivize users to take actions to maximize the overall activity in a
social network? In this paper, we model the number of online actions over time
usin... | computer science |
11,873 | Raw Waveform-based Speech Enhancement by Fully Convolutional Networks | stat.ML | This study proposes a fully convolutional network (FCN) model for raw
waveform-based speech enhancement. The proposed system performs speech
enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which
dif-fers from most existing denoising methods that process the magnitude
spectrum (e.g., log power ... | computer science |
11,874 | Convolutional Recurrent Neural Networks for Bird Audio Detection | cs.SD | Bird sounds possess distinctive spectral structure which may exhibit small
shifts in spectrum depending on the bird species and environmental conditions.
In this paper, we propose using convolutional recurrent neural networks on the
task of automated bird audio detection in real-life environments. In the
proposed metho... | computer science |
11,875 | Unsupervised learning of phase transitions: from principal component
analysis to variational autoencoders | cs.LG | We employ unsupervised machine learning techniques to learn latent parameters
which best describe states of the two-dimensional Ising model and the
three-dimensional XY model. These methods range from principal component
analysis to artificial neural network based variational autoencoders. The
states are sampled using ... | computer science |
11,876 | Streaming Weak Submodularity: Interpreting Neural Networks on the Fly | stat.ML | In many machine learning applications, it is important to explain the
predictions of a black-box classifier. For example, why does a deep neural
network assign an image to a particular class? We cast interpretability of
black-box classifiers as a combinatorial maximization problem and propose an
efficient streaming alg... | computer science |
11,877 | Sparse Quadratic Logistic Regression in Sub-quadratic Time | stat.ML | We consider support recovery in the quadratic logistic regression setting -
where the target depends on both p linear terms $x_i$ and up to $p^2$ quadratic
terms $x_i x_j$. Quadratic terms enable prediction/modeling of higher-order
effects between features and the target, but when incorporated naively may
involve solvi... | computer science |
11,878 | On Approximation Guarantees for Greedy Low Rank Optimization | stat.ML | We provide new approximation guarantees for greedy low rank matrix estimation
under standard assumptions of restricted strong convexity and smoothness. Our
novel analysis also uncovers previously unknown connections between the low
rank estimation and combinatorial optimization, so much so that our bounds are
reminisce... | computer science |
11,879 | Scalable Greedy Feature Selection via Weak Submodularity | stat.ML | Greedy algorithms are widely used for problems in machine learning such as
feature selection and set function optimization. Unfortunately, for large
datasets, the running time of even greedy algorithms can be quite high. This is
because for each greedy step we need to refit a model or calculate a function
using the pre... | computer science |
11,880 | Model-Based Policy Search for Automatic Tuning of Multivariate PID
Controllers | cs.LG | PID control architectures are widely used in industrial applications. Despite
their low number of open parameters, tuning multiple, coupled PID controllers
can become tedious in practice. In this paper, we extend PILCO, a model-based
policy search framework, to automatically tune multivariate PID controllers
purely bas... | computer science |
11,881 | Compressed Sensing using Generative Models | stat.ML | The goal of compressed sensing is to estimate a vector from an
underdetermined system of noisy linear measurements, by making use of prior
knowledge on the structure of vectors in the relevant domain. For almost all
results in this literature, the structure is represented by sparsity in a
well-chosen basis. We show how... | computer science |
11,882 | Joint Embedding of Graphs | stat.AP | Feature extraction and dimension reduction for networks is critical in a wide
variety of domains. Efficiently and accurately learning features for multiple
graphs has important applications in statistical inference on graphs. We
propose a method to jointly embed multiple undirected graphs. Given a set of
graphs, the jo... | computer science |
11,883 | Learning Large-Scale Bayesian Networks with the sparsebn Package | stat.ML | Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package ca... | computer science |
11,884 | Continual Learning Through Synaptic Intelligence | cs.LG | While deep learning has led to remarkable advances across diverse
applications, it struggles in domains where the data distribution changes over
the course of learning. In stark contrast, biological neural networks
continually adapt to changing domains, possibly by leveraging complex molecular
machinery to solve many t... | computer science |
11,885 | On the benefits of output sparsity for multi-label classification | math.ST | The multi-label classification framework, where each observation can be
associated with a set of labels, has generated a tremendous amount of attention
over recent years. The modern multi-label problems are typically large-scale in
terms of number of observations, features and labels, and the amount of labels
can even ... | computer science |
11,886 | Separation of time scales and direct computation of weights in deep
neural networks | cs.LG | Artificial intelligence is revolutionizing our lives at an ever increasing
pace. At the heart of this revolution is the recent advancements in deep neural
networks (DNN), learning to perform sophisticated, high-level tasks. However,
training DNNs requires massive amounts of data and is very computationally
intensive. G... | computer science |
11,887 | Matched bipartite block model with covariates | cs.SI | Community detection or clustering is a fundamental task in the analysis of
network data. Many real networks have a bipartite structure which makes
community detection challenging. In this paper, we consider a model which
allows for matched communities in the bipartite setting, in addition to node
covariates with inform... | computer science |
11,888 | Online Learning for Distribution-Free Prediction | cs.LG | We develop an online learning method for prediction, which is important in
problems with large and/or streaming data sets. We formulate the learning
approach using a covariance-fitting methodology, and show that the resulting
predictor has desirable computational and distribution-free properties: It is
implemented onli... | computer science |
11,889 | Selective Harvesting over Networks | cs.SI | Active search (AS) on graphs focuses on collecting certain labeled nodes
(targets) given global knowledge of the network topology and its edge weights
under a query budget. However, in most networks, nodes, topology and edge
weights are all initially unknown. We introduce selective harvesting, a variant
of AS where the... | computer science |
11,890 | A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators
for Partition Function Computation in Log-Linear Models | stat.ML | Log-linear models are arguably the most successful class of graphical models
for large-scale applications because of their simplicity and tractability.
Learning and inference with these models require calculating the partition
function, which is a major bottleneck and intractable for large state spaces.
Importance Samp... | computer science |
11,891 | Block CUR : Decomposing Large Distributed Matrices | stat.ML | A common problem in large-scale data analysis is to approximate a matrix
using a combination of specifically sampled rows and columns, known as CUR
decomposition. Unfortunately, in many real-world environments, the ability to
sample specific individual rows or columns of the matrix is limited by either
system constrain... | computer science |
11,892 | Deep Tensor Encoding | cs.IR | Learning an encoding of feature vectors in terms of an over-complete
dictionary or a information geometric (Fisher vectors) construct is wide-spread
in statistical signal processing and computer vision. In content based
information retrieval using deep-learning classifiers, such encodings are
learnt on the flattened la... | computer science |
11,893 | Spectrum Estimation from a Few Entries | stat.ML | Singular values of a data in a matrix form provide insights on the structure
of the data, the effective dimensionality, and the choice of hyper-parameters
on higher-level data analysis tools. However, in many practical applications
such as collaborative filtering and network analysis, we only get a partial
observation.... | computer science |
11,894 | Independence clustering (without a matrix) | cs.LG | The independence clustering problem is considered in the following
formulation: given a set $S$ of random variables, it is required to find the
finest partitioning $\{U_1,\dots,U_k\}$ of $S$ into clusters such that the
clusters $U_1,\dots,U_k$ are mutually independent. Since mutual independence is
the target, pairwise ... | computer science |
11,895 | Tactics of Adversarial Attack on Deep Reinforcement Learning Agents | cs.LG | We introduce two tactics to attack agents trained by deep reinforcement
learning algorithms using adversarial examples, namely the strategically-timed
attack and the enchanting attack. In the strategically-timed attack, the
adversary aims at minimizing the agent's reward by only attacking the agent at
a small subset of... | computer science |
11,896 | Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient
Descent | cs.LG | In this paper, we propose a novel sufficient decrease technique for variance
reduced stochastic gradient descent methods such as SAG, 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
variance redu... | computer science |
11,897 | Counterfactual Fairness | stat.ML | Machine learning can impact people with legal or ethical consequences when it
is used to automate decisions in areas such as insurance, lending, hiring, and
predictive policing. In many of these scenarios, previous decisions have been
made that are unfairly biased against certain subpopulations, for example those
of a ... | computer science |
11,898 | Stochastic Primal Dual Coordinate Method with Non-Uniform Sampling Based
on Optimality Violations | stat.ML | We study primal-dual type stochastic optimization algorithms with non-uniform
sampling. Our main theoretical contribution in this paper is to present a
convergence analysis of Stochastic Primal Dual Coordinate (SPDC) Method with
arbitrary sampling. Based on this theoretical framework, we propose Optimality
Violation-ba... | computer science |
11,899 | From safe screening rules to working sets for faster Lasso-type solvers | stat.ML | Convex sparsity-promoting regularizations are ubiquitous in modern
statistical learning. By construction, they yield solutions with few non-zero
coefficients, which correspond to saturated constraints in the dual
optimization formulation. Working set (WS) strategies are generic optimization
techniques that consist in s... | computer science |
11,900 | Clustering for Different Scales of Measurement - the Gap-Ratio Weighted
K-means Algorithm | cs.LG | This paper describes a method for clustering data that are spread out over
large regions and which dimensions are on different scales of measurement. Such
an algorithm was developed to implement a robotics application consisting in
sorting and storing objects in an unsupervised way. The toy dataset used to
validate suc... | computer science |
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