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11,201 | The Learnability of Unknown Quantum Measurements | cs.LG | Quantum machine learning has received significant attention in recent years,
and promising progress has been made in the development of quantum algorithms
to speed up traditional machine learning tasks. In this work, however, we focus
on investigating the information-theoretic upper bounds of sample complexity -
how ma... | computer science |
11,202 | Reinforcement Learning and Nonparametric Detection of Game-Theoretic
Equilibrium Play in Social Networks | cs.GT | This paper studies two important signal processing aspects of equilibrium
behavior in non-cooperative games arising in social networks, namely,
reinforcement learning and detection of equilibrium play. The first part of the
paper presents a reinforcement learning (adaptive filtering) algorithm that
facilitates learning... | computer science |
11,203 | Unbiased Bayes for Big Data: Paths of Partial Posteriors | stat.ML | A key quantity of interest in Bayesian inference are expectations of
functions with respect to a posterior distribution. Markov Chain Monte Carlo is
a fundamental tool to consistently compute these expectations via averaging
samples drawn from an approximate posterior. However, its feasibility is being
challenged in th... | computer science |
11,204 | Dirichlet Process Parsimonious Mixtures for clustering | stat.ML | The parsimonious Gaussian mixture models, which exploit an eigenvalue
decomposition of the group covariance matrices of the Gaussian mixture, have
shown their success in particular in cluster analysis. Their estimation is in
general performed by maximum likelihood estimation and has also been considered
from a parametr... | computer science |
11,205 | Understanding Kernel Ridge Regression: Common behaviors from simple
functions to density functionals | cs.LG | Accurate approximations to density functionals have recently been obtained
via machine learning (ML). By applying ML to a simple function of one variable
without any random sampling, we extract the qualitative dependence of errors on
hyperparameters. We find universal features of the behavior in extreme limits,
includi... | computer science |
11,206 | A Bayesian alternative to mutual information for the hierarchical
clustering of dependent random variables | stat.ML | The use of mutual information as a similarity measure in agglomerative
hierarchical clustering (AHC) raises an important issue: some correction needs
to be applied for the dimensionality of variables. In this work, we formulate
the decision of merging dependent multivariate normal variables in an AHC
procedure as a Bay... | computer science |
11,207 | Bayesian Learning for Low-Rank matrix reconstruction | stat.ML | We develop latent variable models for Bayesian learning based low-rank matrix
completion and reconstruction from linear measurements. For under-determined
systems, the developed methods are shown to reconstruct low-rank matrices when
neither the rank nor the noise power is known a-priori. We derive relations
between th... | computer science |
11,208 | Randomized sketches for kernels: Fast and optimal non-parametric
regression | stat.ML | Kernel ridge regression (KRR) is a standard method for performing
non-parametric regression over reproducing kernel Hilbert spaces. Given $n$
samples, the time and space complexity of computing the KRR estimate scale as
$\mathcal{O}(n^3)$ and $\mathcal{O}(n^2)$ respectively, and so is prohibitive
in many cases. We prop... | computer science |
11,209 | Online Optimization : Competing with Dynamic Comparators | cs.LG | Recent literature on online learning has focused on developing adaptive
algorithms that take advantage of a regularity of the sequence of observations,
yet retain worst-case performance guarantees. A complementary direction is to
develop prediction methods that perform well against complex benchmarks. In
this paper, we... | computer science |
11,210 | Noisy Tensor Completion via the Sum-of-Squares Hierarchy | cs.LG | In the noisy tensor completion problem we observe $m$ entries (whose location
is chosen uniformly at random) from an unknown $n_1 \times n_2 \times n_3$
tensor $T$. We assume that $T$ is entry-wise close to being rank $r$. Our goal
is to fill in its missing entries using as few observations as possible. Let $n
= \max(n... | computer science |
11,211 | Online Nonparametric Regression with General Loss Functions | stat.ML | This paper establishes minimax rates for online regression with arbitrary
classes of functions and general losses. We show that below a certain threshold
for the complexity of the function class, the minimax rates depend on both the
curvature of the loss function and the sequential complexities of the class.
Above this... | computer science |
11,212 | Computing Functions of Random Variables via Reproducing Kernel Hilbert
Space Representations | stat.ML | We describe a method to perform functional operations on probability
distributions of random variables. The method uses reproducing kernel Hilbert
space representations of probability distributions, and it is applicable to all
operations which can be applied to points drawn from the respective
distributions. We refer t... | computer science |
11,213 | Sequential Probability Assignment with Binary Alphabets and Large
Classes of Experts | cs.IT | We analyze the problem of sequential probability assignment for binary
outcomes with side information and logarithmic loss, where regret---or,
redundancy---is measured with respect to a (possibly infinite) class of
experts. We provide upper and lower bounds for minimax regret in terms of
sequential complexities of the ... | computer science |
11,214 | The Gram-Charlier A Series based Extended Rule-of-Thumb for Bandwidth
Selection in Univariate and Multivariate Kernel Density Estimations | cs.LG | The article derives a novel Gram-Charlier A (GCA) Series based Extended
Rule-of-Thumb (ExROT) for bandwidth selection in Kernel Density Estimation
(KDE). There are existing various bandwidth selection rules achieving
minimization of the Asymptotic Mean Integrated Square Error (AMISE) between the
estimated probability d... | computer science |
11,215 | Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via
Eigenvector and Semidefinite Programming Synchronization | cs.LG | We consider the classic problem of establishing a statistical ranking of a
set of n items given a set of inconsistent and incomplete pairwise comparisons
between such items. Instantiations of this problem occur in numerous
applications in data analysis (e.g., ranking teams in sports data), computer
vision, and machine ... | computer science |
11,216 | Deciding when to stop: Efficient stopping of active learning guided
drug-target prediction | cs.LG | Active learning has shown to reduce the number of experiments needed to
obtain high-confidence drug-target predictions. However, in order to actually
save experiments using active learning, it is crucial to have a method to
evaluate the quality of the current prediction and decide when to stop the
experimentation proce... | computer science |
11,217 | Gradient of Probability Density Functions based Contrasts for Blind
Source Separation (BSS) | cs.LG | The article derives some novel independence measures and contrast functions
for Blind Source Separation (BSS) application. For the $k^{th}$ order
differentiable multivariate functions with equal hyper-volumes (region bounded
by hyper-surfaces) and with a constraint of bounded support for $k>1$, it
proves that equality ... | computer science |
11,218 | Diffusion Component Analysis: Unraveling Functional Topology in
Biological Networks | cs.LG | Complex biological systems have been successfully modeled by biochemical and
genetic interaction networks, typically gathered from high-throughput (HTP)
data. These networks can be used to infer functional relationships between
genes or proteins. Using the intuition that the topological role of a gene in a
network rela... | computer science |
11,219 | Actively Learning to Attract Followers on Twitter | stat.ML | Twitter, a popular social network, presents great opportunities for on-line
machine learning research. However, previous research has focused almost
entirely on learning from passively collected data. We study the problem of
learning to acquire followers through normative user behavior, as opposed to
the mass following... | computer science |
11,220 | Non-Uniform Stochastic Average Gradient Method for Training Conditional
Random Fields | stat.ML | We apply stochastic average gradient (SAG) algorithms for training
conditional random fields (CRFs). We describe a practical implementation that
uses structure in the CRF gradient to reduce the memory requirement of this
linearly-convergent stochastic gradient method, propose a non-uniform sampling
scheme that substant... | computer science |
11,221 | Nonparametric Nearest Neighbor Random Process Clustering | stat.ML | We consider the problem of clustering noisy finite-length observations of
stationary ergodic random processes according to their nonparametric generative
models without prior knowledge of the model statistics and the number of
generative models. Two algorithms, both using the L1-distance between estimated
power spectra... | computer science |
11,222 | Poisson Matrix Recovery and Completion | cs.LG | We extend the theory of low-rank matrix recovery and completion to the case
when Poisson observations for a linear combination or a subset of the entries
of a matrix are available, which arises in various applications with count
data. We consider the usual matrix recovery formulation through maximum
likelihood with pro... | computer science |
11,223 | Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares
Algorithms | cs.DS | Tensor rank and low-rank tensor decompositions have many applications in
learning and complexity theory. Most known algorithms use unfoldings of tensors
and can only handle rank up to $n^{\lfloor p/2 \rfloor}$ for a $p$-th order
tensor in $\mathbb{R}^{n^p}$. Previously no efficient algorithm can decompose
3rd order ten... | computer science |
11,224 | Can FCA-based Recommender System Suggest a Proper Classifier? | cs.IR | The paper briefly introduces multiple classifier systems and describes a new
algorithm, which improves classification accuracy by means of recommendation of
a proper algorithm to an object classification. This recommendation is done
assuming that a classifier is likely to predict the label of the object
correctly if it... | computer science |
11,225 | Spectral Norm of Random Kernel Matrices with Applications to Privacy | stat.ML | Kernel methods are an extremely popular set of techniques used for many
important machine learning and data analysis applications. In addition to
having good practical performances, these methods are supported by a
well-developed theory. Kernel methods use an implicit mapping of the input data
into a high dimensional f... | computer science |
11,226 | Regularization-free estimation in trace regression with symmetric
positive semidefinite matrices | stat.ML | Over the past few years, trace regression models have received considerable
attention in the context of matrix completion, quantum state tomography, and
compressed sensing. Estimation of the underlying matrix from
regularization-based approaches promoting low-rankedness, notably nuclear norm
regularization, have enjoye... | computer science |
11,227 | Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion | cs.SI | Social trust prediction addresses the significant problem of exploring
interactions among users in social networks. Naturally, this problem can be
formulated in the matrix completion framework, with each entry indicating the
trustness or distrustness. However, there are two challenges for the social
trust problem: 1) t... | computer science |
11,228 | Overlapping Communities Detection via Measure Space Embedding | cs.LG | We present a new algorithm for community detection. The algorithm uses random
walks to embed the graph in a space of measures, after which a modification of
$k$-means in that space is applied. The algorithm is therefore fast and easily
parallelizable. We evaluate the algorithm on standard random graph benchmarks,
inclu... | computer science |
11,229 | Assessing binary classifiers using only positive and unlabeled data | stat.ML | Assessing the performance of a learned model is a crucial part of machine
learning. However, in some domains only positive and unlabeled examples are
available, which prohibits the use of most standard evaluation metrics. We
propose an approach to estimate any metric based on contingency tables,
including ROC and PR cu... | computer science |
11,230 | Sign Stable Random Projections for Large-Scale Learning | stat.ML | We study the use of "sign $\alpha$-stable random projections" (where
$0<\alpha\leq 2$) for building basic data processing tools in the context of
large-scale machine learning applications (e.g., classification, regression,
clustering, and near-neighbor search). After the processing by sign stable
random projections, th... | computer science |
11,231 | Explaining the Success of AdaBoost and Random Forests as Interpolating
Classifiers | stat.ML | There is a large literature explaining why AdaBoost is a successful
classifier. The literature on AdaBoost focuses on classifier margins and
boosting's interpretation as the optimization of an exponential likelihood
function. These existing explanations, however, have been pointed out to be
incomplete. A random forest ... | computer science |
11,232 | Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than
Random Selection | math.OC | There has been significant recent work on the theory and application of
randomized coordinate descent algorithms, beginning with the work of Nesterov
[SIAM J. Optim., 22(2), 2012], who showed that a random-coordinate selection
rule achieves the same convergence rate as the Gauss-Southwell selection rule.
This result su... | computer science |
11,233 | An objective prior that unifies objective Bayes and information-based
inference | stat.ML | There are three principle paradigms of statistical inference: (i) Bayesian,
(ii) information-based and (iii) frequentist inference. We describe an
objective prior (the weighting or $w$-prior) which unifies objective Bayes and
information-based inference. The $w$-prior is chosen to make the marginal
probability an unbia... | computer science |
11,234 | The Preference Learning Toolbox | stat.ML | Preference learning (PL) is a core area of machine learning that handles
datasets with ordinal relations. As the number of generated data of ordinal
nature is increasing, the importance and role of the PL field becomes central
within machine learning research and practice. This paper introduces an open
source, scalable... | computer science |
11,235 | Communication Complexity of Distributed Convex Learning and Optimization | cs.LG | We study the fundamental limits to communication-efficient distributed
methods for convex learning and optimization, under different assumptions on
the information available to individual machines, and the types of functions
considered. We identify cases where existing algorithms are already worst-case
optimal, as well... | computer science |
11,236 | Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives | cs.LG | Many classical algorithms are found until several years later to outlive the
confines in which they were conceived, and continue to be relevant in
unforeseen settings. In this paper, we show that SVRG is one such method: being
originally designed for strongly convex objectives, it is also very robust in
non-strongly co... | computer science |
11,237 | Gene selection for cancer classification using a hybrid of univariate
and multivariate feature selection methods | cs.CE | Various approaches to gene selection for cancer classification based on
microarray data can be found in the literature and they may be grouped into two
categories: univariate methods and multivariate methods. Univariate methods
look at each gene in the data in isolation from others. They measure the
contribution of a p... | computer science |
11,238 | Global Gene Expression Analysis Using Machine Learning Methods | cs.CE | Microarray is a technology to quantitatively monitor the expression of large
number of genes in parallel. It has become one of the main tools for global
gene expression analysis in molecular biology research in recent years. The
large amount of expression data generated by this technology makes the study of
certain com... | computer science |
11,239 | Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex
Losses | math.OC | In this work we develop a new algorithm for regularized empirical risk
minimization. Our method extends recent techniques of Shalev-Shwartz [02/2015],
which enable a dual-free analysis of SDCA, to arbitrary mini-batching schemes.
Moreover, our method is able to better utilize the information in the data
defining the ER... | computer science |
11,240 | DUAL-LOCO: Distributing Statistical Estimation Using Random Projections | stat.ML | We present DUAL-LOCO, a communication-efficient algorithm for distributed
statistical estimation. DUAL-LOCO assumes that the data is distributed
according to the features rather than the samples. It requires only a single
round of communication where low-dimensional random projections are used to
approximate the depend... | computer science |
11,241 | Variational Dropout and the Local Reparameterization Trick | stat.ML | We investigate a local reparameterizaton technique for greatly reducing the
variance of stochastic gradients for variational Bayesian inference (SGVB) of a
posterior over model parameters, while retaining parallelizability. This local
reparameterization translates uncertainty about global parameters into local
noise th... | computer science |
11,242 | Distributed Training of Structured SVM | stat.ML | Training structured prediction models is time-consuming. However, most
existing approaches only use a single machine, thus, the advantage of computing
power and the capacity for larger data sets of multiple machines have not been
exploited. In this work, we propose an efficient algorithm for distributedly
training stru... | computer science |
11,243 | Stagewise Learning for Sparse Clustering of Discretely-Valued Data | stat.ML | The performance of EM in learning mixtures of product distributions often
depends on the initialization. This can be problematic in crowdsourcing and
other applications, e.g. when a small number of 'experts' are diluted by a
large number of noisy, unreliable participants. We develop a new EM algorithm
that is driven by... | computer science |
11,244 | Measuring Sample Quality with Stein's Method | stat.ML | To improve the efficiency of Monte Carlo estimation, practitioners are
turning to biased Markov chain Monte Carlo procedures that trade off asymptotic
exactness for computational speed. The reasoning is sound: a reduction in
variance due to more rapid sampling can outweigh the bias introduced. However,
the inexactness ... | computer science |
11,245 | Clustering by transitive propagation | cs.LG | We present a global optimization algorithm for clustering data given the
ratio of likelihoods that each pair of data points is in the same cluster or in
different clusters. To define a clustering solution in terms of pairwise
relationships, a necessary and sufficient condition is that belonging to the
same cluster sati... | computer science |
11,246 | Provable Bayesian Inference via Particle Mirror Descent | cs.LG | Bayesian methods are appealing in their flexibility in modeling complex data
and ability in capturing uncertainty in parameters. However, when Bayes' rule
does not result in tractable closed-form, most approximate inference algorithms
lack either scalability or rigorous guarantees. To tackle this challenge, we
propose ... | computer science |
11,247 | Symmetric Tensor Completion from Multilinear Entries and Learning
Product Mixtures over the Hypercube | cs.DS | We give an algorithm for completing an order-$m$ symmetric low-rank tensor
from its multilinear entries in time roughly proportional to the number of
tensor entries. We apply our tensor completion algorithm to the problem of
learning mixtures of product distributions over the hypercube, obtaining new
algorithmic result... | computer science |
11,248 | Copula variational inference | stat.ML | We develop a general variational inference method that preserves dependency
among the latent variables. Our method uses copulas to augment the families of
distributions used in mean-field and structured approximations. Copulas model
the dependency that is not captured by the original variational distribution,
and thus ... | computer science |
11,249 | Matrix Completion from Fewer Entries: Spectral Detectability and Rank
Estimation | cs.LG | The completion of low rank matrices from few entries is a task with many
practical applications. We consider here two aspects of this problem:
detectability, i.e. the ability to estimate the rank $r$ reliably from the
fewest possible random entries, and performance in achieving small
reconstruction error. We propose a ... | computer science |
11,250 | Variance Reduced Stochastic Gradient Descent with Neighbors | cs.LG | Stochastic Gradient Descent (SGD) is a workhorse in machine learning, yet its
slow convergence can be a computational bottleneck. Variance reduction
techniques such as SAG, SVRG and SAGA have been proposed to overcome this
weakness, achieving linear convergence. However, these methods are either based
on computations o... | computer science |
11,251 | GAP Safe screening rules for sparse multi-task and multi-class models | stat.ML | High dimensional regression benefits from sparsity promoting regularizations.
Screening rules leverage the known sparsity of the solution by ignoring some
variables in the optimization, hence speeding up solvers. When the procedure is
proven not to discard features wrongly the rules are said to be \emph{safe}. In
this ... | computer science |
11,252 | Reducing offline evaluation bias of collaborative filtering algorithms | cs.IR | Recommendation systems have been integrated into the majority of large online
systems to filter and rank information according to user profiles. It thus
influences the way users interact with the system and, as a consequence, bias
the evaluation of the performance of a recommendation algorithm computed using
historical... | computer science |
11,253 | A Flexible and Efficient Algorithmic Framework for Constrained Matrix
and Tensor Factorization | stat.ML | We propose a general algorithmic framework for constrained matrix and tensor
factorization, which is widely used in signal processing and machine learning.
The new framework is a hybrid between alternating optimization (AO) and the
alternating direction method of multipliers (ADMM): each matrix factor is
updated in tur... | computer science |
11,254 | Spectral Sparsification and Regret Minimization Beyond Matrix
Multiplicative Updates | cs.LG | In this paper, we provide a novel construction of the linear-sized spectral
sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous
constructions required $\Omega(n^4)$ running time [BSS14, Zou12], our
sparsification routine can be implemented in almost-quadratic running time
$O(n^{2+\varepsilon})$.
Th... | computer science |
11,255 | Feature Selection for Ridge Regression with Provable Guarantees | stat.ML | We introduce single-set spectral sparsification as a deterministic sampling
based feature selection technique for regularized least squares classification,
which is the classification analogue to ridge regression. The method is
unsupervised and gives worst-case guarantees of the generalization power of the
classificati... | computer science |
11,256 | Information-based inference for singular models and finite sample sizes | stat.ML | A central problem in statistics is model selection, the choice between
competing models of a stochastic process whose observables are corrupted by
noise. In the information-based paradigm of inference, model selection is
performed by estimating the predictive performance of the com- peting models.
The candidate model w... | computer science |
11,257 | Variational Gaussian Copula Inference | stat.ML | We utilize copulas to constitute a unified framework for constructing and
optimizing variational proposals in hierarchical Bayesian models. For models
with continuous and non-Gaussian hidden variables, we propose a semiparametric
and automated variational Gaussian copula approach, in which the parametric
Gaussian copul... | computer science |
11,258 | Approximate Inference with the Variational Holder Bound | stat.ML | We introduce the Variational Holder (VH) bound as an alternative to
Variational Bayes (VB) for approximate Bayesian inference. Unlike VB which
typically involves maximization of a non-convex lower bound with respect to the
variational parameters, the VH bound involves minimization of a convex upper
bound to the intract... | computer science |
11,259 | A simple application of FIC to model selection | cs.LG | We have recently proposed a new information-based approach to model
selection, the Frequentist Information Criterion (FIC), that reconciles
information-based and frequentist inference. The purpose of this current paper
is to provide a simple example of the application of this criterion and a
demonstration of the natura... | computer science |
11,260 | Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms | cs.LG | Stochastic gradient descent (SGD) is a ubiquitous algorithm for a variety of
machine learning problems. Researchers and industry have developed several
techniques to optimize SGD's runtime performance, including asynchronous
execution and reduced precision. Our main result is a martingale-based analysis
that enables us... | computer science |
11,261 | PAC-Bayes Iterated Logarithm Bounds for Martingale Mixtures | cs.LG | We give tight concentration bounds for mixtures of martingales that are
simultaneously uniform over (a) mixture distributions, in a PAC-Bayes sense;
and (b) all finite times. These bounds are proved in terms of the martingale
variance, extending classical Bernstein inequalities, and sharpening and
simplifying prior wor... | computer science |
11,262 | Non-Normal Mixtures of Experts | stat.ME | Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in
data for regression, classification and clustering. For continuous data which
we consider here in the context of regression and cluster analysis, MoE usually
use normal experts, that is, expert components following the Gaussian
distribution. ... | computer science |
11,263 | Graphs in machine learning: an introduction | stat.ML | Graphs are commonly used to characterise interactions between objects of
interest. Because they are based on a straightforward formalism, they are used
in many scientific fields from computer science to historical sciences. In this
paper, we give an introduction to some methods relying on graphs for learning.
This incl... | computer science |
11,264 | GEFCOM 2014 - Probabilistic Electricity Price Forecasting | stat.ML | Energy price forecasting is a relevant yet hard task in the field of
multi-step time series forecasting. In this paper we compare a well-known and
established method, ARMA with exogenous variables with a relatively new
technique Gradient Boosting Regression. The method was tested on data from
Global Energy Forecasting ... | computer science |
11,265 | Benchmark of structured machine learning methods for microbial
identification from mass-spectrometry data | stat.ML | Microbial identification is a central issue in microbiology, in particular in
the fields of infectious diseases diagnosis and industrial quality control. The
concept of species is tightly linked to the concept of biological and clinical
classification where the proximity between species is generally measured in
terms o... | computer science |
11,266 | Un-regularizing: approximate proximal point and faster stochastic
algorithms for empirical risk minimization | stat.ML | We develop a family of accelerated stochastic algorithms that minimize sums
of convex functions. Our algorithms improve upon the fastest running time for
empirical risk minimization (ERM), and in particular linear least-squares
regression, across a wide range of problem settings. To achieve this, we
establish a framewo... | computer science |
11,267 | Global Optimality in Tensor Factorization, Deep Learning, and Beyond | cs.NA | Techniques involving factorization are found in a wide range of applications
and have enjoyed significant empirical success in many fields. However, common
to a vast majority of these problems is the significant disadvantage that the
associated optimization problems are typically non-convex due to a multilinear
form or... | computer science |
11,268 | Manifold Optimization for Gaussian Mixture Models | stat.ML | We take a new look at parameter estimation for Gaussian Mixture Models
(GMMs). In particular, we propose using \emph{Riemannian manifold optimization}
as a powerful counterpart to Expectation Maximization (EM). An out-of-the-box
invocation of manifold optimization, however, fails spectacularly: it converges
to the same... | computer science |
11,269 | Diffusion Nets | stat.ML | Non-linear manifold learning enables high-dimensional data analysis, but
requires out-of-sample-extension methods to process new data points. In this
paper, we propose a manifold learning algorithm based on deep learning to
create an encoder, which maps a high-dimensional dataset and its
low-dimensional embedding, and ... | computer science |
11,270 | Collaboratively Learning Preferences from Ordinal Data | cs.LG | In applications such as recommendation systems and revenue management, it is
important to predict preferences on items that have not been seen by a user or
predict outcomes of comparisons among those that have never been compared. A
popular discrete choice model of multinomial logit model captures the structure
of the ... | computer science |
11,271 | A spectral method for community detection in moderately-sparse
degree-corrected stochastic block models | math.PR | We consider community detection in Degree-Corrected Stochastic Block Models
(DC-SBM). We propose a spectral clustering algorithm based on a suitably
normalized adjacency matrix. We show that this algorithm consistently recovers
the block-membership of all but a vanishing fraction of nodes, in the regime
where the lowes... | computer science |
11,272 | Portfolio optimization using local linear regression ensembles in
RapidMiner | cs.LG | In this paper we implement a Local Linear Regression Ensemble Committee
(LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The
estimates and the historical returns of the committees are used to compute the
weights of the portfolio from the 453 stock. The proposed method outperforms
benchmark portfol... | computer science |
11,273 | Learning Single Index Models in High Dimensions | stat.ML | Single Index Models (SIMs) are simple yet flexible semi-parametric models for
classification and regression. Response variables are modeled as a nonlinear,
monotonic function of a linear combination of features. Estimation in this
context requires learning both the feature weights, and the nonlinear function.
While met... | computer science |
11,274 | Framework for Multi-task Multiple Kernel Learning and Applications in
Genome Analysis | stat.ML | We present a general regularization-based framework for Multi-task learning
(MTL), in which the similarity between tasks can be learned or refined using
$\ell_p$-norm Multiple Kernel learning (MKL). Based on this very general
formulation (including a general loss function), we derive the corresponding
dual formulation ... | computer science |
11,275 | DC Proximal Newton for Non-Convex Optimization Problems | cs.LG | We introduce a novel algorithm for solving learning problems where both the
loss function and the regularizer are non-convex but belong to the class of
difference of convex (DC) functions. Our contribution is a new general purpose
proximal Newton algorithm that is able to deal with such a situation. The
algorithm consi... | computer science |
11,276 | Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret
Analysis | math.OC | We consider the correlated multiarmed bandit (MAB) problem in which the
rewards associated with each arm are modeled by a multivariate Gaussian random
variable, and we investigate the influence of the assumptions in the Bayesian
prior on the performance of the upper credible limit (UCL) algorithm and a new
correlated U... | computer science |
11,277 | Scan $B$-Statistic for Kernel Change-Point Detection | cs.LG | Detecting the emergence of an abrupt change-point is a classic problem in
statistics and machine learning. Kernel-based nonparametric statistics have
been used for this task which enjoy fewer assumptions on the distributions than
the parametric approach and can handle high-dimensional data. In this paper we
focus on th... | computer science |
11,278 | Robust Sparse Blind Source Separation | stat.AP | Blind Source Separation is a widely used technique to analyze multichannel
data. In many real-world applications, its results can be significantly
hampered by the presence of unknown outliers. In this paper, a novel algorithm
coined rGMCA (robust Generalized Morphological Component Analysis) is
introduced to retrieve s... | computer science |
11,279 | Optimal approximate matrix product in terms of stable rank | cs.DS | We prove, using the subspace embedding guarantee in a black box way, that one
can achieve the spectral norm guarantee for approximate matrix multiplication
with a dimensionality-reducing map having $m = O(\tilde{r}/\varepsilon^2)$
rows. Here $\tilde{r}$ is the maximum stable rank, i.e. squared ratio of
Frobenius and op... | computer science |
11,280 | The Information Sieve | stat.ML | We introduce a new framework for unsupervised learning of representations
based on a novel hierarchical decomposition of information. Intuitively, data
is passed through a series of progressively fine-grained sieves. Each layer of
the sieve recovers a single latent factor that is maximally informative about
multivariat... | computer science |
11,281 | COEVOLVE: A Joint Point Process Model for Information Diffusion and
Network Co-evolution | cs.SI | Information diffusion in online social networks is affected by the underlying
network topology, but it also has the power to change it. Online users are
constantly creating new links when exposed to new information sources, and in
turn these links are alternating the way information spreads. However, these
two highly i... | computer science |
11,282 | Locally Non-linear Embeddings for Extreme Multi-label Learning | cs.LG | The objective in extreme multi-label learning is to train a classifier that
can automatically tag a novel data point with the most relevant subset of
labels from an extremely large label set. Embedding based approaches make
training and prediction tractable by assuming that the training label matrix is
low-rank and hen... | computer science |
11,283 | Adaptive Mixtures of Factor Analyzers | stat.ML | A mixture of factor analyzers is a semi-parametric density estimator that
generalizes the well-known mixtures of Gaussians model by allowing each
Gaussian in the mixture to be represented in a different lower-dimensional
manifold. This paper presents a robust and parsimonious model selection
algorithm for training a mi... | computer science |
11,284 | A Review of Nonnegative Matrix Factorization Methods for Clustering | stat.ML | Nonnegative Matrix Factorization (NMF) was first introduced as a low-rank
matrix approximation technique, and has enjoyed a wide area of applications.
Although NMF does not seem related to the clustering problem at first, it was
shown that they are closely linked. In this report, we provide a gentle
introduction to clu... | computer science |
11,285 | Tensor principal component analysis via sum-of-squares proofs | cs.LG | We study a statistical model for the tensor principal component analysis
problem introduced by Montanari and Richard: Given a order-$3$ tensor $T$ of
the form $T = \tau \cdot v_0^{\otimes 3} + A$, where $\tau \geq 0$ is a
signal-to-noise ratio, $v_0$ is a unit vector, and $A$ is a random noise
tensor, the goal is to re... | computer science |
11,286 | Parallel MMF: a Multiresolution Approach to Matrix Computation | cs.NA | Multiresolution Matrix Factorization (MMF) was recently introduced as a
method for finding multiscale structure and defining wavelets on
graphs/matrices. In this paper we derive pMMF, a parallel algorithm for
computing the MMF factorization. Empirically, the running time of pMMF scales
linearly in the dimension for spa... | computer science |
11,287 | Learning to classify with possible sensor failures | cs.LG | In this paper, we propose a general framework to learn a robust large-margin
binary classifier when corrupt measurements, called anomalies, caused by sensor
failure might be present in the training set. The goal is to minimize the
generalization error of the classifier on non-corrupted measurements while
controlling th... | computer science |
11,288 | Variational Gram Functions: Convex Analysis and Optimization | math.OC | We propose a new class of convex penalty functions, called \emph{variational
Gram functions} (VGFs), that can promote pairwise relations, such as
orthogonality, among a set of vectors in a vector space. These functions can
serve as regularizers in convex optimization problems arising from hierarchical
classification, m... | computer science |
11,289 | On the Minimax Risk of Dictionary Learning | stat.ML | We consider the problem of learning a dictionary matrix from a number of
observed signals, which are assumed to be generated via a linear model with a
common underlying dictionary. In particular, we derive lower bounds on the
minimum achievable worst case mean squared error (MSE), regardless of
computational complexity... | computer science |
11,290 | On the Worst-Case Approximability of Sparse PCA | stat.ML | It is well known that Sparse PCA (Sparse Principal Component Analysis) is
NP-hard to solve exactly on worst-case instances. What is the complexity of
solving Sparse PCA approximately? Our contributions include: 1) a simple and
efficient algorithm that achieves an $n^{-1/3}$-approximation; 2) NP-hardness
of approximatio... | computer science |
11,291 | Evaluation of Spectral Learning for the Identification of Hidden Markov
Models | stat.ML | Hidden Markov models have successfully been applied as models of discrete
time series in many fields. Often, when applied in practice, the parameters of
these models have to be estimated. The currently predominating identification
methods, such as maximum-likelihood estimation and especially
expectation-maximization, a... | computer science |
11,292 | Dynamic Matrix Factorization with Priors on Unknown Values | stat.ML | Advanced and effective collaborative filtering methods based on explicit
feedback assume that unknown ratings do not follow the same model as the
observed ones (\emph{not missing at random}). In this work, we build on this
assumption, and introduce a novel dynamic matrix factorization framework that
allows to set an ex... | computer science |
11,293 | Supervised Collective Classification for Crowdsourcing | cs.SI | Crowdsourcing utilizes the wisdom of crowds for collective classification via
information (e.g., labels of an item) provided by labelers. Current
crowdsourcing algorithms are mainly unsupervised methods that are unaware of
the quality of crowdsourced data. In this paper, we propose a supervised
collective classificatio... | computer science |
11,294 | Linear Contextual Bandits with Knapsacks | cs.LG | We consider the linear contextual bandit problem with resource consumption,
in addition to reward generation. In each round, the outcome of pulling an arm
is a reward as well as a vector of resource consumptions. The expected values
of these outcomes depend linearly on the context of that arm. The
budget/capacity const... | computer science |
11,295 | Differentially Private Analysis of Outliers | stat.ML | This paper investigates differentially private analysis of distance-based
outliers. The problem of outlier detection is to find a small number of
instances that are apparently distant from the remaining instances. On the
other hand, the objective of differential privacy is to conceal presence (or
absence) of any partic... | computer science |
11,296 | Dimensionality-reduced subspace clustering | stat.ML | Subspace clustering refers to the problem of clustering unlabeled
high-dimensional data points into a union of low-dimensional linear subspaces,
whose number, orientations, and dimensions are all unknown. In practice one may
have access to dimensionality-reduced observations of the data only, resulting,
e.g., from unde... | computer science |
11,297 | Distributed Stochastic Variance Reduced Gradient Methods and A Lower
Bound for Communication Complexity | math.OC | We study distributed optimization algorithms for minimizing the average of
convex functions. The applications include empirical risk minimization problems
in statistical machine learning where the datasets are large and have to be
stored on different machines. We design a distributed stochastic variance
reduced gradien... | computer science |
11,298 | An algorithm for online tensor prediction | stat.ML | We present a new method for online prediction and learning of tensors
($N$-way arrays, $N >2$) from sequential measurements. We focus on the specific
case of 3-D tensors and exploit a recently developed framework of structured
tensor decompositions proposed in [1]. In this framework it is possible to
treat 3-D tensors ... | computer science |
11,299 | STC Anti-spoofing Systems for the ASVspoof 2015 Challenge | cs.SD | This paper presents the Speech Technology Center (STC) systems submitted to
Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof)
Challenge 2015. In this work we investigate different acoustic feature spaces
to determine reliable and robust countermeasures against spoofing attacks. In
addition to the c... | computer science |
11,300 | An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with
Two-Point Feedback | cs.LG | We consider the closely related problems of bandit convex optimization with
two-point feedback, and zero-order stochastic convex optimization with two
function evaluations per round. We provide a simple algorithm and analysis
which is optimal for convex Lipschitz functions. This improves on
\cite{dujww13}, which only p... | computer science |
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