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11,701 | Sampled Fictitious Play is Hannan Consistent | cs.GT | Fictitious play is a simple and widely studied adaptive heuristic for playing
repeated games. It is well known that fictitious play fails to be Hannan
consistent. Several variants of fictitious play including regret matching,
generalized regret matching and smooth fictitious play, are known to be Hannan
consistent. In ... | computer science |
11,702 | Efficient L1-Norm Principal-Component Analysis via Bit Flipping | cs.DS | It was shown recently that the $K$ L1-norm principal components (L1-PCs) of a
real-valued data matrix $\mathbf X \in \mathbb R^{D \times N}$ ($N$ data
samples of $D$ dimensions) can be exactly calculated with cost
$\mathcal{O}(2^{NK})$ or, when advantageous, $\mathcal{O}(N^{dK - K + 1})$
where $d=\mathrm{rank}(\mathbf ... | computer science |
11,703 | Stochastic Averaging for Constrained Optimization with Application to
Online Resource Allocation | math.OC | Existing approaches to resource allocation for nowadays stochastic networks
are challenged to meet fast convergence and tolerable delay requirements. The
present paper leverages online learning advances to facilitate stochastic
resource allocation tasks. By recognizing the central role of Lagrange
multipliers, the unde... | computer science |
11,704 | SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs | cs.DC | Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete
count data such as text and images. Applications require LDA to handle both
large datasets and a large number of topics. Though distributed CPU systems
have been used, GPU-based systems have emerged as a promising alternative
because of the high... | computer science |
11,705 | Distributed Convex Optimization with Many Convex Constraints | math.OC | We address the problem of solving convex optimization problems with many
convex constraints in a distributed setting. Our approach is based on an
extension of the alternating direction method of multipliers (ADMM) that
recently gained a lot of attention in the Big Data context. Although it has
been invented decades ago... | computer science |
11,706 | Sketching Meets Random Projection in the Dual: A Provable Recovery
Algorithm for Big and High-dimensional Data | cs.LG | Sketching techniques have become popular for scaling up machine learning
algorithms by reducing the sample size or dimensionality of massive data sets,
while still maintaining the statistical power of big data. In this paper, we
study sketching from an optimization point of view: we first show that the
iterative Hessia... | computer science |
11,707 | Learning in Implicit Generative Models | stat.ML | Generative adversarial networks (GANs) provide an algorithmic framework for
constructing generative models with several appealing properties: they do not
require a likelihood function to be specified, only a generating procedure;
they provide samples that are sharp and compelling; and they allow us to
harness our knowl... | computer science |
11,708 | Parallelizing Stochastic Approximation Through Mini-Batching and
Tail-Averaging | stat.ML | This work characterizes the benefits of averaging techniques widely used in
conjunction with stochastic gradient descent (SGD). In particular, this work
sharply analyzes: (1) mini-batching, a method of averaging many samples of the
gradient to both reduce the variance of a stochastic gradient estimate and for
paralleli... | computer science |
11,709 | Dictionary Update for NMF-based Voice Conversion Using an
Encoder-Decoder Network | stat.ML | In this paper, we propose a dictionary update method for Nonnegative Matrix
Factorization (NMF) with high dimensional data in a spectral conversion (SC)
task. Voice conversion has been widely studied due to its potential
applications such as personalized speech synthesis and speech enhancement.
Exemplar-based NMF (ENMF... | computer science |
11,710 | Voice Conversion from Non-parallel Corpora Using Variational
Auto-encoder | stat.ML | We propose a flexible framework for spectral conversion (SC) that facilitates
training with unaligned corpora. Many SC frameworks require parallel corpora,
phonetic alignments, or explicit frame-wise correspondence for learning
conversion functions or for synthesizing a target spectrum with the aid of
alignments. Howev... | computer science |
11,711 | MML is not consistent for Neyman-Scott | stat.ML | Strict Minimum Message Length (SMML) is a statistical inference method widely
cited (but only with informal arguments) as providing estimations that are
consistent for general estimation problems. It is, however, almost invariably
intractable to compute, for which reason only approximations of it (known as
MML algorith... | computer science |
11,712 | Semi-supervised Graph Embedding Approach to Dynamic Link Prediction | stat.ML | We propose a simple discrete time semi-supervised graph embedding approach to
link prediction in dynamic networks. The learned embedding reflects information
from both the temporal and cross-sectional network structures, which is
performed by defining the loss function as a weighted sum of the supervised
loss from past... | computer science |
11,713 | Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection,
and Extreme Bandits | cs.LG | We present a novel distribution-free approach, the data-driven threshold
machine (DTM), for a fundamental problem at the core of many learning tasks:
choose a threshold for a given pre-specified level that bounds the tail
probability of the maximum of a (possibly dependent but stationary) random
sequence. We do not ass... | computer science |
11,714 | Dynamic Stacked Generalization for Node Classification on Networks | stat.ML | We propose a novel stacked generalization (stacking) method as a dynamic
ensemble technique using a pool of heterogeneous classifiers for node label
classification on networks. The proposed method assigns component models a set
of functional coefficients, which can vary smoothly with certain topological
features of a n... | computer science |
11,715 | Decentralized Collaborative Learning of Personalized Models over
Networks | cs.LG | We consider a set of learning agents in a collaborative peer-to-peer network,
where each agent learns a personalized model according to its own learning
objective. The question addressed in this paper is: how can agents improve upon
their locally trained model by communicating with other agents that have
similar object... | computer science |
11,716 | A probabilistic model for the numerical solution of initial value
problems | math.NA | Like many numerical methods, solvers for initial value problems (IVPs) on
ordinary differential equations estimate an analytically intractable quantity,
using the results of tractable computations as inputs. This structure is
closely connected to the notion of inference on latent variables in statistics.
We describe a ... | computer science |
11,717 | Analysis and Implementation of an Asynchronous Optimization Algorithm
for the Parameter Server | math.OC | This paper presents an asynchronous incremental aggregated gradient algorithm
and its implementation in a parameter server framework for solving regularized
optimization problems. The algorithm can handle both general convex (possibly
non-smooth) regularizers and general convex constraints. When the empirical
data loss... | computer science |
11,718 | Semi-supervised Knowledge Transfer for Deep Learning from Private
Training Data | stat.ML | Some machine learning applications involve training data that is sensitive,
such as the medical histories of patients in a clinical trial. A model may
inadvertently and implicitly store some of its training data; careful analysis
of the model may therefore reveal sensitive information.
To address this problem, we dem... | computer science |
11,719 | RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks | stat.ML | Users in social networks whose posts stay at the top of their followers'{}
feeds the longest time are more likely to be noticed. Can we design an online
algorithm to help them decide when to post to stay at the top? In this paper,
we address this question as a novel optimal control problem for jump stochastic
different... | computer science |
11,720 | Modeling the Dynamics of Online Learning Activity | stat.ML | People are increasingly relying on the Web and social media to find solutions
to their problems in a wide range of domains. In this online setting, closely
related problems often lead to the same characteristic learning pattern, in
which people sharing these problems visit related pieces of information,
perform almost ... | computer science |
11,721 | Big Batch SGD: Automated Inference using Adaptive Batch Sizes | cs.LG | Classical stochastic gradient methods for optimization rely on noisy gradient
approximations that become progressively less accurate as iterates approach a
solution. The large noise and small signal in the resulting gradients makes it
difficult to use them for adaptive stepsize selection and automatic stopping.
We prop... | computer science |
11,722 | Membership Inference Attacks against Machine Learning Models | cs.CR | We quantitatively investigate how machine learning models leak information
about the individual data records on which they were trained. We focus on the
basic membership inference attack: given a data record and black-box access to
a model, determine if the record was in the model's training dataset. To
perform members... | computer science |
11,723 | ChoiceRank: Identifying Preferences from Node Traffic in Networks | stat.ML | Understanding how users navigate in a network is of high interest in many
applications. We consider a setting where only aggregate node-level traffic is
observed and tackle the task of learning edge transition probabilities. We cast
it as a preference learning problem, and we study a model where choices follow
Luce's a... | computer science |
11,724 | Combinatorial Multi-Armed Bandit with General Reward Functions | cs.LG | In this paper, we study the stochastic combinatorial multi-armed bandit
(CMAB) framework that allows a general nonlinear reward function, whose
expected value may not depend only on the means of the input random variables
but possibly on the entire distributions of these variables. Our framework
enables a much larger c... | computer science |
11,725 | Cross Device Matching for Online Advertising with Neural Feature
Ensembles : First Place Solution at CIKM Cup 2016 | cs.LG | We describe the 1st place winning approach for the CIKM Cup 2016 Challenge.
In this paper, we provide an approach to reasonably identify same users across
multiple devices based on browsing logs. Our approach regards a candidate
ranking problem as pairwise classification and utilizes an unsupervised neural
feature ense... | computer science |
11,726 | Truncated Variance Reduction: A Unified Approach to Bayesian
Optimization and Level-Set Estimation | stat.ML | We present a new algorithm, truncated variance reduction (TruVaR), that
treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian
processes in a unified fashion. The algorithm greedily shrinks a sum of
truncated variances within a set of potential maximizers (BO) or unclassified
points (LSE), which... | computer science |
11,727 | A Variational Bayesian Approach for Image Restoration. Application to
Image Deblurring with Poisson-Gaussian Noise | math.OC | In this paper, a methodology is investigated for signal recovery in the
presence of non-Gaussian noise. In contrast with regularized minimization
approaches often adopted in the literature, in our algorithm the regularization
parameter is reliably estimated from the observations. As the posterior density
of the unknown... | computer science |
11,728 | Frank-Wolfe Algorithms for Saddle Point Problems | math.OC | We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained
smooth convex-concave saddle point (SP) problems. Remarkably, the method only
requires access to linear minimization oracles. Leveraging recent advances in
FW optimization, we provide the first proof of convergence of a FW-type saddle
point solv... | computer science |
11,729 | Automatic measurement of vowel duration via structured prediction | stat.ML | A key barrier to making phonetic studies scalable and replicable is the need
to rely on subjective, manual annotation. To help meet this challenge, a
machine learning algorithm was developed for automatic measurement of a widely
used phonetic measure: vowel duration. Manually-annotated data were used to
train a model t... | computer science |
11,730 | Things Bayes can't do | cs.LG | The problem of forecasting conditional probabilities of the next event given
the past is considered in a general probabilistic setting. Given an arbitrary
(large, uncountable) set C of predictors, we would like to construct a single
predictor that performs asymptotically as well as the best predictor in C, on
any data.... | computer science |
11,731 | Differentially Private Variational Inference for Non-conjugate Models | stat.ML | Many machine learning applications are based on data collected from people,
such as their tastes and behaviour as well as biological traits and genetic
data. Regardless of how important the application might be, one has to make
sure individuals' identities or the privacy of the data are not compromised in
the analysis.... | computer science |
11,732 | Operator Variational Inference | stat.ML | Variational inference is an umbrella term for algorithms which cast Bayesian
inference as optimization. Classically, variational inference uses the
Kullback-Leibler divergence to define the optimization. Though this divergence
has been widely used, the resultant posterior approximation can suffer from
undesirable stati... | computer science |
11,733 | Toward Implicit Sample Noise Modeling: Deviation-driven Matrix
Factorization | cs.LG | The objective function of a matrix factorization model usually aims to
minimize the average of a regression error contributed by each element.
However, given the existence of stochastic noises, the implicit deviations of
sample data from their true values are almost surely diverse, which makes each
data point not equal... | computer science |
11,734 | Globally Optimal Training of Generalized Polynomial Neural Networks with
Nonlinear Spectral Methods | cs.LG | The optimization problem behind neural networks is highly non-convex.
Training with stochastic gradient descent and variants requires careful
parameter tuning and provides no guarantee to achieve the global optimum. In
contrast we show under quite weak assumptions on the data that a particular
class of feedforward neur... | computer science |
11,735 | Sparse Signal Recovery for Binary Compressed Sensing by Majority Voting
Neural Networks | cs.IT | In this paper, we propose majority voting neural networks for sparse signal
recovery in binary compressed sensing. The majority voting neural network is
composed of several independently trained feedforward neural networks employing
the sigmoid function as an activation function. Our empirical study shows that
a choice... | computer science |
11,736 | Bayesian Adaptive Data Analysis Guarantees from Subgaussianity | cs.LG | The new field of adaptive data analysis seeks to provide algorithms and
provable guarantees for models of machine learning that allow researchers to
reuse their data, which normally falls outside of the usual statistical
paradigm of static data analysis. In 2014, Dwork, Feldman, Hardt, Pitassi,
Reingold and Roth introd... | computer science |
11,737 | Stationary time-vertex signal processing | cs.LG | The goal of this paper is to improve learning for multivariate processes
whose structure is dependent on some known graph topology; especially when the
number of available samples is much smaller than the number of variables.
Typically, the graph information is incorporated into the learning process via
a smoothness as... | computer science |
11,738 | Semi-Supervised Radio Signal Identification | cs.LG | Radio emitter recognition in dense multi-user environments is an important
tool for optimizing spectrum utilization, identifying and minimizing
interference, and enforcing spectrum policy. Radio data is readily available
and easy to obtain from an antenna, but labeled and curated data is often
scarce making supervised ... | computer science |
11,739 | Enhanced Factored Three-Way Restricted Boltzmann Machines for Speech
Detection | cs.SD | In this letter, we propose enhanced factored three way restricted Boltzmann
machines (EFTW-RBMs) for speech detection. The proposed model incorporates
conditional feature learning by multiplying the dynamical state of the third
unit, which allows a modulation over the visible-hidden node pairs. Instead of
stacking prev... | computer science |
11,740 | Variational Inference via $χ$-Upper Bound Minimization | stat.ML | Variational inference (VI) is widely used as an efficient alternative to
Markov chain Monte Carlo. It posits a family of approximating distributions $q$
and finds the closest member to the exact posterior $p$. Closeness is usually
measured via a divergence $D(q || p)$ from $q$ to $p$. While successful, this
approach al... | computer science |
11,741 | Stochastic Variational Deep Kernel Learning | stat.ML | Deep kernel learning combines the non-parametric flexibility of kernel
methods with the inductive biases of deep learning architectures. We propose a
novel deep kernel learning model and stochastic variational inference procedure
which generalizes deep kernel learning approaches to enable classification,
multi-task lea... | computer science |
11,742 | Computationally Efficient Influence Maximization in Stochastic and
Adversarial Models: Algorithms and Analysis | cs.SI | We consider the problem of influence maximization in fixed networks, for both
stochastic and adversarial contagion models. The common goal is to select a
subset of nodes of a specified size to infect so that the number of infected
nodes at the conclusion of the epidemic is as large as possible. In the
stochastic settin... | computer science |
11,743 | Fast Eigenspace Approximation using Random Signals | cs.DS | We focus in this work on the estimation of the first $k$ eigenvectors of any
graph Laplacian using filtering of Gaussian random signals. We prove that we
only need $k$ such signals to be able to exactly recover as many of the
smallest eigenvectors, regardless of the number of nodes in the graph. In
addition, we address... | computer science |
11,744 | Multitask Protein Function Prediction Through Task Dissimilarity | stat.ML | Automated protein function prediction is a challenging problem with
distinctive features, such as the hierarchical organization of protein
functions and the scarcity of annotated proteins for most biological functions.
We propose a multitask learning algorithm addressing both issues. Unlike
standard multitask algorithm... | computer science |
11,745 | PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring
Numerical Optimizers | cs.LG | Safeguarding privacy in machine learning is highly desirable, especially in
collaborative studies across many organizations. Privacy-preserving distributed
machine learning (based on cryptography) is popular to solve the problem.
However, existing cryptographic protocols still incur excess computational
overhead. Here,... | computer science |
11,746 | Information Dropout: Learning Optimal Representations Through Noisy
Computation | stat.ML | The cross-entropy loss commonly used in deep learning is closely related to
the defining properties of optimal representations, but does not enforce some
of the key properties. We show that this can be solved by adding a
regularization term, which is in turn related to injecting multiplicative noise
in the activations ... | computer science |
11,747 | Learning heat diffusion graphs | cs.LG | Effective information analysis generally boils down to properly identifying
the structure or geometry of the data, which is often represented by a graph.
In some applications, this structure may be partly determined by design
constraints or pre-determined sensing arrangements, like in road transportation
networks for e... | computer science |
11,748 | Distributed Coordinate Descent for Generalized Linear Models with
Regularization | stat.ML | Generalized linear model with $L_1$ and $L_2$ regularization is a widely used
technique for solving classification, class probability estimation and
regression problems. With the numbers of both features and examples growing
rapidly in the fields like text mining and clickstream data analysis
parallelization and the us... | computer science |
11,749 | Learning Influence Functions from Incomplete Observations | cs.SI | We study the problem of learning influence functions under incomplete
observations of node activations. Incomplete observations are a major concern
as most (online and real-world) social networks are not fully observable. We
establish both proper and improper PAC learnability of influence functions
under randomly missi... | computer science |
11,750 | Recovery Guarantee of Non-negative Matrix Factorization via Alternating
Updates | cs.LG | Non-negative matrix factorization is a popular tool for decomposing data into
feature and weight matrices under non-negativity constraints. It enjoys
practical success but is poorly understood theoretically. This paper proposes
an algorithm that alternates between decoding the weights and updating the
features, and sho... | computer science |
11,751 | An Introduction to MM Algorithms for Machine Learning and Statistical | stat.CO | MM (majorization--minimization) algorithms are an increasingly popular tool
for solving optimization problems in machine learning and statistical
estimation. This article introduces the MM algorithm framework in general and
via three popular example applications: Gaussian mixture regressions,
multinomial logistic regre... | computer science |
11,752 | Riemannian Tensor Completion with Side Information | stat.ML | By restricting the iterate on a nonlinear manifold, the recently proposed
Riemannian optimization methods prove to be both efficient and effective in low
rank tensor completion problems. However, existing methods fail to exploit the
easily accessible side information, due to their format mismatch. Consequently,
there i... | computer science |
11,753 | On numerical approximation schemes for expectation propagation | stat.CO | Several numerical approximation strategies for the expectation-propagation
algorithm are studied in the context of large-scale learning: the Laplace
method, a faster variant of it, Gaussian quadrature, and a deterministic
version of variational sampling (i.e., combining quadrature with variational
approximation). Exper... | computer science |
11,754 | Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann
Machines | cs.LG | Quantum annealing (QA) is a hardware-based heuristic optimization and
sampling method applicable to discrete undirected graphical models. While
similar to simulated annealing, QA relies on quantum, rather than thermal,
effects to explore complex search spaces. For many classes of problems, QA is
known to offer computat... | computer science |
11,755 | The Power of Normalization: Faster Evasion of Saddle Points | cs.LG | A commonly used heuristic in non-convex optimization is Normalized Gradient
Descent (NGD) - a variant of gradient descent in which only the direction of
the gradient is taken into account and its magnitude ignored. We analyze this
heuristic and show that with carefully chosen parameters and noise injection,
this method... | computer science |
11,756 | Oracle Complexity of Second-Order Methods for Finite-Sum Problems | math.OC | Finite-sum optimization problems are ubiquitous in machine learning, and are
commonly solved using first-order methods which rely on gradient computations.
Recently, there has been growing interest in \emph{second-order} methods, which
rely on both gradients and Hessians. In principle, second-order methods can
require ... | computer science |
11,757 | Algebraic multigrid support vector machines | stat.ML | The support vector machine is a flexible optimization-based technique widely
used for classification problems. In practice, its training part becomes
computationally expensive on large-scale data sets because of such reasons as
the complexity and number of iterations in parameter fitting methods,
underlying optimizatio... | computer science |
11,758 | Gap Safe screening rules for sparsity enforcing penalties | stat.ML | In high dimensional regression settings, sparsity enforcing penalties have
proved useful to regularize the data-fitting term. A recently introduced
technique called screening rules propose to ignore some variables in the
optimization leveraging the expected sparsity of the solutions and consequently
leading to faster s... | computer science |
11,759 | Robust and Scalable Column/Row Sampling from Corrupted Big Data | cs.LG | Conventional sampling techniques fall short of drawing descriptive sketches
of the data when the data is grossly corrupted as such corruptions break the
low rank structure required for them to perform satisfactorily. In this paper,
we present new sampling algorithms which can locate the informative columns in
presence ... | computer science |
11,760 | GaDei: On Scale-up Training As A Service For Deep Learning | stat.ML | Deep learning (DL) training-as-a-service (TaaS) is an important emerging
industrial workload. The unique challenge of TaaS is that it must satisfy a
wide range of customers who have no experience and resources to tune DL
hyper-parameters, and meticulous tuning for each user's dataset is
prohibitively expensive. Therefo... | computer science |
11,761 | Deep Clustering and Conventional Networks for Music Separation: Stronger
Together | stat.ML | Deep clustering is the first method to handle general audio separation
scenarios with multiple sources of the same type and an arbitrary number of
sources, performing impressively in speaker-independent speech separation
tasks. However, little is known about its effectiveness in other challenging
situations such as mus... | computer science |
11,762 | Variational Boosting: Iteratively Refining Posterior Approximations | stat.ML | We propose a black-box variational inference method to approximate
intractable distributions with an increasingly rich approximating class. Our
method, termed variational boosting, iteratively refines an existing
variational approximation by solving a sequence of optimization problems,
allowing the practitioner to trad... | computer science |
11,763 | Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring | cs.LG | We present a new notion of probabilistic duality for random variables
involving mixture distributions. Using this notion, we show how to implement a
highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise
graphical models with strictly positive factors that requires almost no
preprocessing and is easy ... | computer science |
11,764 | Emergence of Compositional Representations in Restricted Boltzmann
Machines | cs.LG | Extracting automatically the complex set of features composing real
high-dimensional data is crucial for achieving high performance in
machine--learning tasks. Restricted Boltzmann Machines (RBM) are empirically
known to be efficient for this purpose, and to be able to generate distributed
and graded representations of... | computer science |
11,765 | Measuring Sample Quality with Diffusions | stat.ML | Stein's method for measuring convergence to a continuous target distribution
relies on an operator characterizing the target and Stein factor bounds on the
solutions of an associated differential equation. While such operators and
bounds are readily available for a diversity of univariate targets, few
multivariate targ... | computer science |
11,766 | The Recycling Gibbs Sampler for Efficient Learning | stat.CO | Monte Carlo methods are essential tools for Bayesian inference. Gibbs
sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively
used in signal processing, machine learning, and statistics, employed to draw
samples from complicated high-dimensional posterior distributions. The key
point for the suc... | computer science |
11,767 | A Neural Network Model to Classify Liver Cancer Patients Using Data
Expansion and Compression | stat.ML | We develop a neural network model to classify liver cancer patients into
high-risk and low-risk groups using genomic data. Our approach provides a novel
technique to classify big data sets using neural network models. We preprocess
the data before training the neural network models. We first expand the data
using wavel... | computer science |
11,768 | EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer
Interfaces | cs.LG | Brain computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This signal is generally chosen
from a variety of well-studied electroencephalogram (EEG) signals. For a given
BCI paradigm, feature extractors and classifiers are tailored to the distinct
character... | computer science |
11,769 | A Unified Convex Surrogate for the Schatten-$p$ Norm | stat.ML | The Schatten-$p$ norm ($0<p<1$) has been widely used to replace the nuclear
norm for better approximating the rank function. However, existing methods are
either 1) not scalable for large scale problems due to relying on singular
value decomposition (SVD) in every iteration, or 2) specific to some $p$
values, e.g., $1/... | computer science |
11,770 | Patient-Driven Privacy Control through Generalized Distillation | cs.CR | The introduction of data analytics into medicine has changed the nature of
patient treatment. In this, patients are asked to disclose personal information
such as genetic markers, lifestyle habits, and clinical history. This data is
then used by statistical models to predict personalized treatments. However,
due to pri... | computer science |
11,771 | Machine Learning on Human Connectome Data from MRI | cs.LG | Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging
modalities that allow in-vivo analysis of a patient's brain network (known as a
connectome). Use of these technologies has enabled faster and better diagnoses
and treatments of neurological disorders and a deeper understanding of the
human brain. R... | computer science |
11,772 | Associative Memory using Dictionary Learning and Expander Decoding | stat.ML | An associative memory is a framework of content-addressable memory that
stores a collection of message vectors (or a dataset) over a neural network
while enabling a neurally feasible mechanism to recover any message in the
dataset from its noisy version. Designing an associative memory requires
addressing two main task... | computer science |
11,773 | Learning Features of Music from Scratch | stat.ML | This paper introduces a new large-scale music dataset, MusicNet, to serve as
a source of supervision and evaluation of machine learning methods for music
research. MusicNet consists of hundreds of freely-licensed classical music
recordings by 10 composers, written for 11 instruments, together with
instrument/note annot... | computer science |
11,774 | Subsampled online matrix factorization with convergence guarantees | math.OC | We present a matrix factorization algorithm that scales to input matrices
that are large in both dimensions (i.e., that contains morethan 1TB of data).
The algorithm streams the matrix columns while subsampling them, resulting in
low complexity per iteration andreasonable memory footprint. In contrast to
previous onlin... | computer science |
11,775 | Reliably Learning the ReLU in Polynomial Time | cs.LG | We give the first dimension-efficient algorithms for learning Rectified
Linear Units (ReLUs), which are functions of the form $\mathbf{x} \mapsto
\max(0, \mathbf{w} \cdot \mathbf{x})$ with $\mathbf{w} \in \mathbb{S}^{n-1}$.
Our algorithm works in the challenging Reliable Agnostic learning model of
Kalai, Kanade, and Ma... | computer science |
11,776 | Influential Node Detection in Implicit Social Networks using Multi-task
Gaussian Copula Models | cs.SI | Influential node detection is a central research topic in social network
analysis. Many existing methods rely on the assumption that the network
structure is completely known \textit{a priori}. However, in many applications,
network structure is unavailable to explain the underlying information
diffusion phenomenon. To... | computer science |
11,777 | Robust method for finding sparse solutions to linear inverse problems
using an L2 regularization | cs.NA | We analyzed the performance of a biologically inspired algorithm called the
Corrected Projections Algorithm (CPA) when a sparseness constraint is required
to unambiguously reconstruct an observed signal using atoms from an
overcomplete dictionary. By changing the geometry of the estimation problem,
CPA gives an analyti... | computer science |
11,778 | Clustering Signed Networks with the Geometric Mean of Laplacians | stat.ML | Signed networks allow to model positive and negative relationships. We
analyze existing extensions of spectral clustering to signed networks. It turns
out that existing approaches do not recover the ground truth clustering in
several situations where either the positive or the negative network structures
contain no noi... | computer science |
11,779 | Outlier Detection for Text Data : An Extended Version | cs.IR | The problem of outlier detection is extremely challenging in many domains
such as text, in which the attribute values are typically non-negative, and
most values are zero. In such cases, it often becomes difficult to separate the
outliers from the natural variations in the patterns in the underlying data. In
this paper... | computer science |
11,780 | Signed Laplacian for spectral clustering revisited | cs.DS | Classical spectral clustering is based on a spectral decomposition of a graph
Laplacian, obtained from a graph adjacency matrix representing positive graph
edge weights describing similarities of graph vertices. In signed graphs, the
graph edge weights can be negative to describe disparities of graph vertices,
for exam... | computer science |
11,781 | Follow the Compressed Leader: Faster Online Learning of Eigenvectors and
Faster MMWU | cs.LG | The online problem of computing the top eigenvector is fundamental to machine
learning. In both adversarial and stochastic settings, previous results (such
as matrix multiplicative weight update, follow the regularized leader, follow
the compressed leader, block power method) either achieve optimal regret but
run slow,... | computer science |
11,782 | Deep driven fMRI decoding of visual categories | stat.ML | Deep neural networks have been developed drawing inspiration from the brain
visual pathway, implementing an end-to-end approach: from image data to video
object classes. However building an fMRI decoder with the typical structure of
Convolutional Neural Network (CNN), i.e. learning multiple level of
representations, se... | computer science |
11,783 | Machine Learning of Linear Differential Equations using Gaussian
Processes | cs.LG | This work leverages recent advances in probabilistic machine learning to
discover conservation laws expressed by parametric linear equations. Such
equations involve, but are not limited to, ordinary and partial differential,
integro-differential, and fractional order operators. Here, Gaussian process
priors are modifie... | computer science |
11,784 | Identifying Best Interventions through Online Importance Sampling | stat.ML | Motivated by applications in computational advertising and systems biology,
we consider the problem of identifying the best out of several possible soft
interventions at a source node $V$ in an acyclic causal directed graph, to
maximize the expected value of a target node $Y$ (located downstream of $V$).
Our setting im... | computer science |
11,785 | Manifold Alignment Determination: finding correspondences across
different data views | stat.ML | We present Manifold Alignment Determination (MAD), an algorithm for learning
alignments between data points from multiple views or modalities. The approach
is capable of learning correspondences between views as well as correspondences
between individual data-points. The proposed method requires only a few aligned
exam... | computer science |
11,786 | Perishability of Data: Dynamic Pricing under Varying-Coefficient Models | cs.GT | We consider a firm that sells a large number of products to its customers in
an online fashion. Each product is described by a high dimensional feature
vector, and the market value of a product is assumed to be linear in the values
of its features. Parameters of the valuation model are unknown and can change
over time.... | computer science |
11,787 | Diffusion-based nonlinear filtering for multimodal data fusion with
application to sleep stage assessment | stat.ML | The problem of information fusion from multiple data-sets acquired by
multimodal sensors has drawn significant research attention over the years. In
this paper, we focus on a particular problem setting consisting of a physical
phenomenon or a system of interest observed by multiple sensors. We assume that
all sensors m... | computer science |
11,788 | An Online Convex Optimization Approach to Dynamic Network Resource
Allocation | cs.SY | Existing approaches to online convex optimization (OCO) make sequential
one-slot-ahead decisions, which lead to (possibly adversarial) losses that
drive subsequent decision iterates. Their performance is evaluated by the
so-called regret that measures the difference of losses between the online
solution and the best ye... | computer science |
11,789 | Deep Learning for Computational Chemistry | stat.ML | The rise and fall of artificial neural networks is well documented in the
scientific literature of both computer science and computational chemistry. Yet
almost two decades later, we are now seeing a resurgence of interest in deep
learning, a machine learning algorithm based on multilayer neural networks.
Within the la... | computer science |
11,790 | Recommendation under Capacity Constraints | stat.ML | In this paper, we investigate the common scenario where every candidate item
for recommendation is characterized by a maximum capacity, i.e., number of
seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the
prevalence of the task of recommending items under capacity constraints in a
variety of s... | computer science |
11,791 | Stochastic Subsampling for Factorizing Huge Matrices | stat.ML | We present a matrix-factorization algorithm that scales to input matrices
with both huge number of rows and columns. Learned factors may be sparse or
dense and/or non-negative, which makes our algorithm suitable for dictionary
learning, sparse component analysis, and non-negative matrix factorization. Our
algorithm str... | computer science |
11,792 | Fisher consistency for prior probability shift | stat.ML | We introduce Fisher consistency in the sense of unbiasedness as a desirable
property for estimators of class prior probabilities. Lack of Fisher
consistency could be used as a criterion to dismiss estimators that are
unlikely to deliver precise estimates in test datasets under prior probability
and more general dataset... | computer science |
11,793 | Disentangling group and link persistence in Dynamic Stochastic Block
models | cs.SI | We study the inference of a model of dynamic networks in which both
communities and links keep memory of previous network states. By considering
maximum likelihood inference from single snapshot observations of the network,
we show that link persistence makes the inference of communities harder,
decreasing the detectab... | computer science |
11,794 | Learning Policies for Markov Decision Processes from Data | math.OC | We consider the problem of learning a policy for a Markov decision process
consistent with data captured on the state-actions pairs followed by the
policy. We assume that the policy belongs to a class of parameterized policies
which are defined using features associated with the state-action pairs. The
features are kno... | computer science |
11,795 | Effective and Extensible Feature Extraction Method Using Genetic
Algorithm-Based Frequency-Domain Feature Search for Epileptic EEG
Multi-classification | cs.LG | In this paper, a genetic algorithm-based frequency-domain feature search
(GAFDS) method is proposed for the electroencephalogram (EEG) analysis of
epilepsy. In this method, frequency-domain features are first searched and then
combined with nonlinear features. Subsequently, these features are selected and
optimized to ... | computer science |
11,796 | Predicting Demographics of High-Resolution Geographies with Geotagged
Tweets | cs.LG | In this paper, we consider the problem of predicting demographics of
geographic units given geotagged Tweets that are composed within these units.
Traditional survey methods that offer demographics estimates are usually
limited in terms of geographic resolution, geographic boundaries, and time
intervals. Thus, it would... | computer science |
11,797 | Robust mixture of experts modeling using the $t$ distribution | stat.ME | Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in
data for regression, classification, and clustering. For regression and cluster
analyses of continuous data, MoE usually use normal experts following the
Gaussian distribution. However, for a set of data containing a group or groups
of observ... | computer science |
11,798 | A Model-based Projection Technique for Segmenting Customers | stat.ME | We consider the problem of segmenting a large population of customers into
non-overlapping groups with similar preferences, using diverse preference
observations such as purchases, ratings, clicks, etc. over subsets of items. We
focus on the setting where the universe of items is large (ranging from
thousands to millio... | computer science |
11,799 | Fast and Accurate Time Series Classification with WEASEL | cs.DS | Time series (TS) occur in many scientific and commercial applications,
ranging from earth surveillance to industry automation to the smart grids. An
important type of TS analysis is classification, which can, for instance,
improve energy load forecasting in smart grids by detecting the types of
electronic devices based... | computer science |
11,800 | Information Theoretic Limits for Linear Prediction with Graph-Structured
Sparsity | cs.LG | We analyze the necessary number of samples for sparse vector recovery in a
noisy linear prediction setup. This model includes problems such as linear
regression and classification. We focus on structured graph models. In
particular, we prove that sufficient number of samples for the weighted graph
model proposed by Heg... | computer science |
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