Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
10,401 | Non-strongly-convex smooth stochastic approximation with convergence
rate O(1/n) | cs.LG | We consider the stochastic approximation problem where a convex function has
to be minimized, given only the knowledge of unbiased estimates of its
gradients at certain points, a framework which includes machine learning
methods based on the minimization of the empirical risk. We focus on problems
without strong convex... | computer science |
10,402 | Generative Model Selection Using a Scalable and Size-Independent Complex
Network Classifier | cs.SI | Real networks exhibit nontrivial topological features such as heavy-tailed
degree distribution, high clustering, and small-worldness. Researchers have
developed several generative models for synthesizing artificial networks that
are structurally similar to real networks. An important research problem is to
identify the... | computer science |
10,403 | Efficient Classification for Metric Data | cs.LG | Recent advances in large-margin classification of data residing in general
metric spaces (rather than Hilbert spaces) enable classification under various
natural metrics, such as string edit and earthmover distance. A general
framework developed for this purpose by von Luxburg and Bousquet [JMLR, 2004]
left open the qu... | computer science |
10,404 | Flexible sampling of discrete data correlations without the marginal
distributions | stat.ML | Learning the joint dependence of discrete variables is a fundamental problem
in machine learning, with many applications including prediction, clustering
and dimensionality reduction. More recently, the framework of copula modeling
has gained popularity due to its modular parametrization of joint
distributions. Among o... | computer science |
10,405 | Bayesian Inference and Learning in Gaussian Process State-Space Models
with Particle MCMC | stat.ML | State-space models are successfully used in many areas of science,
engineering and economics to model time series and dynamical systems. We
present a fully Bayesian approach to inference \emph{and learning} (i.e. state
estimation and system identification) in nonlinear nonparametric state-space
models. We place a Gauss... | computer science |
10,406 | Robust Support Vector Machines for Speaker Verification Task | cs.LG | An important step in speaker verification is extracting features that best
characterize the speaker voice. This paper investigates a front-end processing
that aims at improving the performance of speaker verification based on the
SVMs classifier, in text independent mode. This approach combines features
based on conven... | computer science |
10,407 | Completing Any Low-rank Matrix, Provably | stat.ML | Matrix completion, i.e., the exact and provable recovery of a low-rank matrix
from a small subset of its elements, is currently only known to be possible if
the matrix satisfies a restrictive structural constraint---known as {\em
incoherence}---on its row and column spaces. In these cases, the subset of
elements is sam... | computer science |
10,408 | Dynamic Infinite Mixed-Membership Stochastic Blockmodel | cs.SI | Directional and pairwise measurements are often used to model
inter-relationships in a social network setting. The Mixed-Membership
Stochastic Blockmodel (MMSB) was a seminal work in this area, and many of its
capabilities were extended since then. In this paper, we propose the
\emph{Dynamic Infinite Mixed-Membership s... | computer science |
10,409 | Physeter catodon localization by sparse coding | cs.LG | This paper presents a spermwhale' localization architecture using jointly a
bag-of-features (BoF) approach and machine learning framework. BoF methods are
known, especially in computer vision, to produce from a collection of local
features a global representation invariant to principal signal transformations.
Our idea ... | computer science |
10,410 | Bregman Alternating Direction Method of Multipliers | math.OC | The mirror descent algorithm (MDA) generalizes gradient descent by using a
Bregman divergence to replace squared Euclidean distance. In this paper, we
similarly generalize the alternating direction method of multipliers (ADMM) to
Bregman ADMM (BADMM), which allows the choice of different Bregman divergences
to exploit ... | computer science |
10,411 | Relaxed Sparse Eigenvalue Conditions for Sparse Estimation via
Non-convex Regularized Regression | cs.LG | Non-convex regularizers usually improve the performance of sparse estimation
in practice. To prove this fact, we study the conditions of sparse estimations
for the sharp concave regularizers which are a general family of non-convex
regularizers including many existing regularizers. For the global solutions of
the regul... | computer science |
10,412 | Constrained fractional set programs and their application in local
clustering and community detection | stat.ML | The (constrained) minimization of a ratio of set functions is a problem
frequently occurring in clustering and community detection. As these
optimization problems are typically NP-hard, one uses convex or spectral
relaxations in practice. While these relaxations can be solved globally
optimally, they are often too loos... | computer science |
10,413 | Classifying Single-Trial EEG during Motor Imagery with a Small Training
Set | cs.LG | Before the operation of a motor imagery based brain-computer interface (BCI)
adopting machine learning techniques, a cumbersome training procedure is
unavoidable. The development of a practical BCI posed the challenge of
classifying single-trial EEG with a small training set. In this letter, we
addressed this problem b... | computer science |
10,414 | Outlying Property Detection with Numerical Attributes | cs.LG | The outlying property detection problem is the problem of discovering the
properties distinguishing a given object, known in advance to be an outlier in
a database, from the other database objects. In this paper, we analyze the
problem within a context where numerical attributes are taken into account,
which represents... | computer science |
10,415 | Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data
from Machine Learning Classifiers | cs.CR | Machine Learning (ML) algorithms are used to train computers to perform a
variety of complex tasks and improve with experience. Computers learn how to
recognize patterns, make unintended decisions, or react to a dynamic
environment. Certain trained machines may be more effective than others because
they are based on mo... | computer science |
10,416 | Stochastic Majorization-Minimization Algorithms for Large-Scale
Optimization | stat.ML | Majorization-minimization algorithms consist of iteratively minimizing a
majorizing surrogate of an objective function. Because of its simplicity and
its wide applicability, this principle has been very popular in statistics and
in signal processing. In this paper, we intend to make this principle scalable.
We introduc... | computer science |
10,417 | A Statistical Perspective on Algorithmic Leveraging | stat.ME | One popular method for dealing with large-scale data sets is sampling. For
example, by using the empirical statistical leverage scores as an importance
sampling distribution, the method of algorithmic leveraging samples and
rescales rows/columns of data matrices to reduce the data size before
performing computations on... | computer science |
10,418 | Fourier PCA and Robust Tensor Decomposition | cs.LG | Fourier PCA is Principal Component Analysis of a matrix obtained from higher
order derivatives of the logarithm of the Fourier transform of a
distribution.We make this method algorithmic by developing a tensor
decomposition method for a pair of tensors sharing the same vectors in rank-$1$
decompositions. Our main appli... | computer science |
10,419 | Traffic data reconstruction based on Markov random field modeling | stat.ML | We consider the traffic data reconstruction problem. Suppose we have the
traffic data of an entire city that are incomplete because some road data are
unobserved. The problem is to reconstruct the unobserved parts of the data. In
this paper, we propose a new method to reconstruct incomplete traffic data
collected from ... | computer science |
10,420 | Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and
Stationary $β$-Mixing Processes | cs.LG | Pac-Bayes bounds are among the most accurate generalization bounds for
classifiers learned from independently and identically distributed (IID) data,
and it is particularly so for margin classifiers: there have been recent
contributions showing how practical these bounds can be either to perform model
selection (Ambrol... | computer science |
10,421 | High-dimensional structure estimation in Ising models: Local separation
criterion | stat.ML | We consider the problem of high-dimensional Ising (graphical) model
selection. We propose a simple algorithm for structure estimation based on the
thresholding of the empirical conditional variation distances. We introduce a
novel criterion for tractable graph families, where this method is efficient,
based on the pres... | computer science |
10,422 | Robust Kernel Density Estimation | stat.ML | We propose a method for nonparametric density estimation that exhibits
robustness to contamination of the training sample. This method achieves
robustness by combining a traditional kernel density estimator (KDE) with ideas
from classical $M$-estimation. We interpret the KDE based on a radial, positive
semi-definite ke... | computer science |
10,423 | On Learning Discrete Graphical Models Using Greedy Methods | cs.LG | In this paper, we address the problem of learning the structure of a pairwise
graphical model from samples in a high-dimensional setting. Our first main
result studies the sparsistency, or consistency in sparsity pattern recovery,
properties of a forward-backward greedy algorithm as applied to general
statistical model... | computer science |
10,424 | Composite Self-Concordant Minimization | stat.ML | We propose a variable metric framework for minimizing the sum of a
self-concordant function and a possibly non-smooth convex function, endowed
with an easily computable proximal operator. We theoretically establish the
convergence of our framework without relying on the usual Lipschitz gradient
assumption on the smooth... | computer science |
10,425 | Computational Rationalization: The Inverse Equilibrium Problem | cs.GT | Modeling the purposeful behavior of imperfect agents from a small number of
observations is a challenging task. When restricted to the single-agent
decision-theoretic setting, inverse optimal control techniques assume that
observed behavior is an approximately optimal solution to an unknown decision
problem. These tech... | computer science |
10,426 | Standardizing Interestingness Measures for Association Rules | stat.AP | Interestingness measures provide information that can be used to prune or
select association rules. A given value of an interestingness measure is often
interpreted relative to the overall range of the values that the
interestingness measure can take. However, properties of individual association
rules restrict the val... | computer science |
10,427 | A balanced k-means algorithm for weighted point sets | math.OC | The classical $k$-means algorithm for partitioning $n$ points in
$\mathbb{R}^d$ into $k$ clusters is one of the most popular and widely spread
clustering methods. The need to respect prescribed lower bounds on the cluster
sizes has been observed in many scientific and business applications.
In this paper, we present ... | computer science |
10,428 | Pylearn2: a machine learning research library | stat.ML | Pylearn2 is a machine learning research library. This does not just mean that
it is a collection of machine learning algorithms that share a common API; it
means that it has been designed for flexibility and extensibility in order to
facilitate research projects that involve new or unusual use cases. In this
paper we g... | computer science |
10,429 | Online and stochastic Douglas-Rachford splitting method for large scale
machine learning | cs.NA | Online and stochastic learning has emerged as powerful tool in large scale
optimization. In this work, we generalize the Douglas-Rachford splitting (DRs)
method for minimizing composite functions to online and stochastic settings (to
our best knowledge this is the first time DRs been generalized to sequential
version).... | computer science |
10,430 | Minimal Dirichlet energy partitions for graphs | math.OC | Motivated by a geometric problem, we introduce a new non-convex graph
partitioning objective where the optimality criterion is given by the sum of
the Dirichlet eigenvalues of the partition components. A relaxed formulation is
identified and a novel rearrangement algorithm is proposed, which we show is
strictly decreas... | computer science |
10,431 | Manopt, a Matlab toolbox for optimization on manifolds | cs.MS | Optimization on manifolds is a rapidly developing branch of nonlinear
optimization. Its focus is on problems where the smooth geometry of the search
space can be leveraged to design efficient numerical algorithms. In particular,
optimization on manifolds is well-suited to deal with rank and orthogonality
constraints. S... | computer science |
10,432 | The Lovasz-Bregman Divergence and connections to rank aggregation,
clustering, and web ranking | cs.LG | We extend the recently introduced theory of Lovasz-Bregman (LB) divergences
(Iyer & Bilmes, 2012) in several ways. We show that they represent a distortion
between a 'score' and an 'ordering', thus providing a new view of rank
aggregation and order based clustering with interesting connections to web
ranking. We show h... | computer science |
10,433 | New Algorithms for Learning Incoherent and Overcomplete Dictionaries | cs.DS | In sparse recovery we are given a matrix $A$ (the dictionary) and a vector of
the form $A X$ where $X$ is sparse, and the goal is to recover $X$. This is a
central notion in signal processing, statistics and machine learning. But in
applications such as sparse coding, edge detection, compression and super
resolution, t... | computer science |
10,434 | Online Ranking: Discrete Choice, Spearman Correlation and Other Feedback | cs.LG | Given a set $V$ of $n$ objects, an online ranking system outputs at each time
step a full ranking of the set, observes a feedback of some form and suffers a
loss. We study the setting in which the (adversarial) feedback is an element in
$V$, and the loss is the position (0th, 1st, 2nd...) of the item in the
outputted r... | computer science |
10,435 | Minimum Probability Flow Learning | cs.LG | Fitting probabilistic models to data is often difficult, due to the general
intractability of the partition function and its derivatives. Here we propose a
new parameter estimation technique that does not require computing an
intractable normalization factor or sampling from the equilibrium distribution
of the model. T... | computer science |
10,436 | Statistical Decision Making for Authentication and Intrusion Detection | stat.ML | User authentication and intrusion detection differ from standard
classification problems in that while we have data generated from legitimate
users, impostor or intrusion data is scarce or non-existent. We review existing
techniques for dealing with this problem and propose a novel alternative based
on a principled sta... | computer science |
10,437 | Which graphical models are difficult to learn? | stat.ML | We consider the problem of learning the structure of Ising models (pairwise
binary Markov random fields) from i.i.d. samples. While several methods have
been proposed to accomplish this task, their relative merits and limitations
remain somewhat obscure. By analyzing a number of concrete examples, we show
that low-comp... | computer science |
10,438 | Inference of global clusters from locally distributed data | stat.ME | We consider the problem of analyzing the heterogeneity of clustering
distributions for multiple groups of observed data, each of which is indexed by
a covariate value, and inferring global clusters arising from observations
aggregated over the covariate domain. We propose a novel Bayesian nonparametric
method reposing ... | computer science |
10,439 | Convex Analysis and Optimization with Submodular Functions: a Tutorial | cs.LG | Set-functions appear in many areas of computer science and applied
mathematics, such as machine learning, computer vision, operations research or
electrical networks. Among these set-functions, submodular functions play an
important role, similar to convex functions on vector spaces. In this tutorial,
the theory of sub... | computer science |
10,440 | Robust PCA via Outlier Pursuit | cs.LG | Singular Value Decomposition (and Principal Component Analysis) is one of the
most widely used techniques for dimensionality reduction: successful and
efficiently computable, it is nevertheless plagued by a well-known,
well-documented sensitivity to outliers. Recent work has considered the setting
where each point has ... | computer science |
10,441 | Sparsity regret bounds for individual sequences in online linear
regression | stat.ML | We consider the problem of online linear regression on arbitrary
deterministic sequences when the ambient dimension d can be much larger than
the number of time rounds T. We introduce the notion of sparsity regret bound,
which is a deterministic online counterpart of recent risk bounds derived in
the stochastic setting... | computer science |
10,442 | Classification under Data Contamination with Application to Remote
Sensing Image Mis-registration | stat.ME | This work is motivated by the problem of image mis-registration in remote
sensing and we are interested in determining the resulting loss in the accuracy
of pattern classification. A statistical formulation is given where we propose
to use data contamination to model and understand the phenomenon of image
mis-registrat... | computer science |
10,443 | Reproducing Kernel Banach Spaces with the l1 Norm | stat.ML | Targeting at sparse learning, we construct Banach spaces B of functions on an
input space X with the properties that (1) B possesses an l1 norm in the sense
that it is isometrically isomorphic to the Banach space of integrable functions
on X with respect to the counting measure; (2) point evaluations are continuous
lin... | computer science |
10,444 | Reproducing Kernel Banach Spaces with the l1 Norm II: Error Analysis for
Regularized Least Square Regression | stat.ML | A typical approach in estimating the learning rate of a regularized learning
scheme is to bound the approximation error by the sum of the sampling error,
the hypothesis error and the regularization error. Using a reproducing kernel
space that satisfies the linear representer theorem brings the advantage of
discarding t... | computer science |
10,445 | Efficient First Order Methods for Linear Composite Regularizers | cs.LG | A wide class of regularization problems in machine learning and statistics
employ a regularization term which is obtained by composing a simple convex
function \omega with a linear transformation. This setting includes Group Lasso
methods, the Fused Lasso and other total variation methods, multi-task learning
methods a... | computer science |
10,446 | Dimension-free tail inequalities for sums of random matrices | math.PR | We derive exponential tail inequalities for sums of random matrices with no
dependence on the explicit matrix dimensions. These are similar to the matrix
versions of the Chernoff bound and Bernstein inequality except with the
explicit matrix dimensions replaced by a trace quantity that can be small even
when the dimens... | computer science |
10,447 | Convex and Network Flow Optimization for Structured Sparsity | math.OC | We consider a class of learning problems regularized by a structured
sparsity-inducing norm defined as the sum of l_2- or l_infinity-norms over
groups of variables. Whereas much effort has been put in developing fast
optimization techniques when the groups are disjoint or embedded in a
hierarchy, we address here the ca... | computer science |
10,448 | Cluster Forests | stat.ME | With inspiration from Random Forests (RF) in the context of classification, a
new clustering ensemble method---Cluster Forests (CF) is proposed.
Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good
local clusterings" and then aggregates via spectral clustering to obtain
cluster assignments fo... | computer science |
10,449 | A sufficient condition on monotonic increase of the number of nonzero
entry in the optimizer of L1 norm penalized least-square problem | stat.ML | The $\ell$-1 norm based optimization is widely used in signal processing,
especially in recent compressed sensing theory. This paper studies the solution
path of the $\ell$-1 norm penalized least-square problem, whose constrained
form is known as Least Absolute Shrinkage and Selection Operator (LASSO). A
solution path ... | computer science |
10,450 | On Combining Machine Learning with Decision Making | math.OC | We present a new application and covering number bound for the framework of
"Machine Learning with Operational Costs (MLOC)," which is an exploratory form
of decision theory. The MLOC framework incorporates knowledge about how a
predictive model will be used for a subsequent task, thus combining machine
learning with t... | computer science |
10,451 | Online Learning: Stochastic and Constrained Adversaries | stat.ML | Learning theory has largely focused on two main learning scenarios. The first
is the classical statistical setting where instances are drawn i.i.d. from a
fixed distribution and the second scenario is the online learning, completely
adversarial scenario where adversary at every time step picks the worst
instance to pro... | computer science |
10,452 | Notes on a New Philosophy of Empirical Science | cs.LG | This book presents a methodology and philosophy of empirical science based on
large scale lossless data compression. In this view a theory is scientific if
it can be used to build a data compression program, and it is valuable if it
can compress a standard benchmark database to a small size, taking into account
the len... | computer science |
10,453 | Iterative Reweighted Minimization Methods for $l_p$ Regularized
Unconstrained Nonlinear Programming | math.OC | In this paper we study general $l_p$ regularized unconstrained minimization
problems. In particular, we derive lower bounds for nonzero entries of first-
and second-order stationary points, and hence also of local minimizers of the
$l_p$ minimization problems. We extend some existing iterative reweighted $l_1$
(IRL1) a... | computer science |
10,454 | Sparse LMS via Online Linearized Bregman Iteration | cs.IT | We propose a version of least-mean-square (LMS) algorithm for sparse system
identification. Our algorithm called online linearized Bregman iteration (OLBI)
is derived from minimizing the cumulative prediction error squared along with
an l1-l2 norm regularizer. By systematically treating the non-differentiable
regulariz... | computer science |
10,455 | Evaluation of linear classifiers on articles containing pharmacokinetic
evidence of drug-drug interactions | stat.ML | Background. Drug-drug interaction (DDI) is a major cause of morbidity and
mortality. [...] Biomedical literature mining can aid DDI research by
extracting relevant DDI signals from either the published literature or large
clinical databases. However, though drug interaction is an ideal area for
translational research, ... | computer science |
10,456 | A fast compression-based similarity measure with applications to
content-based image retrieval | stat.ML | Compression-based similarity measures are effectively employed in
applications on diverse data types with a basically parameter-free approach.
Nevertheless, there are problems in applying these techniques to
medium-to-large datasets which have been seldom addressed. This paper proposes
a similarity measure based on com... | computer science |
10,457 | Distributed High Dimensional Information Theoretical Image Registration
via Random Projections | cs.IT | Information theoretical measures, such as entropy, mutual information, and
various divergences, exhibit robust characteristics in image registration
applications. However, the estimation of these quantities is computationally
intensive in high dimensions. On the other hand, consistent estimation from
pairwise distances... | computer science |
10,458 | A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and
Tighter Bound | cs.LG | The CUR matrix decomposition is an important extension of Nystr\"{o}m
approximation to a general matrix. It approximates any data matrix in terms of
a small number of its columns and rows. In this paper we propose a novel
randomized CUR algorithm with an expected relative-error bound. The proposed
algorithm has the adv... | computer science |
10,459 | Privacy Aware Learning | stat.ML | We study statistical risk minimization problems under a privacy model in
which the data is kept confidential even from the learner. In this local
privacy framework, we establish sharp upper and lower bounds on the convergence
rates of statistical estimation procedures. As a consequence, we exhibit a
precise tradeoff be... | computer science |
10,460 | A Fast Distributed Proximal-Gradient Method | cs.DC | We present a distributed proximal-gradient method for optimizing the average
of convex functions, each of which is the private local objective of an agent
in a network with time-varying topology. The local objectives have distinct
differentiable components, but they share a common nondifferentiable component,
which has... | computer science |
10,461 | Inferring clonal evolution of tumors from single nucleotide somatic
mutations | cs.LG | High-throughput sequencing allows the detection and quantification of
frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor
cell populations. In some cases, the evolutionary history and population
frequency of the subclonal lineages of tumor cells present in the sample can be
reconstructed from... | computer science |
10,462 | Markov Determinantal Point Processes | cs.LG | A determinantal point process (DPP) is a random process useful for modeling
the combinatorial problem of subset selection. In particular, DPPs encourage a
random subset Y to contain a diverse set of items selected from a base set Y.
For example, we might use a DPP to display a set of news headlines that are
relevant to... | computer science |
10,463 | Mechanism Design for Cost Optimal PAC Learning in the Presence of
Strategic Noisy Annotators | cs.LG | We consider the problem of Probably Approximate Correct (PAC) learning of a
binary classifier from noisy labeled examples acquired from multiple annotators
(each characterized by a respective classification noise rate). First, we
consider the complete information scenario, where the learner knows the noise
rates of all... | computer science |
10,464 | Spectral Estimation of Conditional Random Graph Models for Large-Scale
Network Data | cs.SI | Generative models for graphs have been typically committed to strong prior
assumptions concerning the form of the modeled distributions. Moreover, the
vast majority of currently available models are either only suitable for
characterizing some particular network properties (such as degree distribution
or clustering coe... | computer science |
10,465 | Response Aware Model-Based Collaborative Filtering | cs.LG | Previous work on recommender systems mainly focus on fitting the ratings
provided by users. However, the response patterns, i.e., some items are rated
while others not, are generally ignored. We argue that failing to observe such
response patterns can lead to biased parameter estimation and sub-optimal model
performanc... | computer science |
10,466 | A Model-Based Approach to Rounding in Spectral Clustering | cs.LG | In spectral clustering, one defines a similarity matrix for a collection of
data points, transforms the matrix to get the Laplacian matrix, finds the
eigenvectors of the Laplacian matrix, and obtains a partition of the data using
the leading eigenvectors. The last step is sometimes referred to as rounding,
where one ne... | computer science |
10,467 | Efficiently Searching for Frustrated Cycles in MAP Inference | cs.DS | Dual decomposition provides a tractable framework for designing algorithms
for finding the most probable (MAP) configuration in graphical models. However,
for many real-world inference problems, the typical decomposition has a large
integrality gap, due to frustrated cycles. One way to tighten the relaxation is
to intr... | computer science |
10,468 | Latent Structured Ranking | cs.LG | Many latent (factorized) models have been proposed for recommendation tasks
like collaborative filtering and for ranking tasks like document or image
retrieval and annotation. Common to all those methods is that during inference
the items are scored independently by their similarity to the query in the
latent embedding... | computer science |
10,469 | Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought
Stressed Plants | cs.LG | Understanding the adaptation process of plants to drought stress is essential
in improving management practices, breeding strategies as well as engineering
viable crops for a sustainable agriculture in the coming decades.
Hyper-spectral imaging provides a particularly promising approach to gain such
understanding since... | computer science |
10,470 | Factorized Multi-Modal Topic Model | cs.LG | Multi-modal data collections, such as corpora of paired images and text
snippets, require analysis methods beyond single-view component and topic
models. For continuous observations the current dominant approach is based on
extensions of canonical correlation analysis, factorizing the variation into
components shared b... | computer science |
10,471 | Pairwise MRF Calibration by Perturbation of the Bethe Reference Point | cs.LG | We investigate different ways of generating approximate solutions to the
pairwise Markov random field (MRF) selection problem. We focus mainly on the
inverse Ising problem, but discuss also the somewhat related inverse Gaussian
problem because both types of MRF are suitable for inference tasks with the
belief propagati... | computer science |
10,472 | Large-Scale Sparse Principal Component Analysis with Application to Text
Data | stat.ML | Sparse PCA provides a linear combination of small number of features that
maximizes variance across data. Although Sparse PCA has apparent advantages
compared to PCA, such as better interpretability, it is generally thought to be
computationally much more expensive. In this paper, we demonstrate the
surprising fact tha... | computer science |
10,473 | Selective Transfer Learning for Cross Domain Recommendation | cs.LG | Collaborative filtering (CF) aims to predict users' ratings on items
according to historical user-item preference data. In many real-world
applications, preference data are usually sparse, which would make models
overfit and fail to give accurate predictions. Recently, several research works
show that by transferring k... | computer science |
10,474 | Tensor decompositions for learning latent variable models | cs.LG | This work considers a computationally and statistically efficient parameter
estimation method for a wide class of latent variable models---including
Gaussian mixture models, hidden Markov models, and latent Dirichlet
allocation---which exploits a certain tensor structure in their low-order
observable moments (typically... | computer science |
10,475 | Linear Convergence of Variance-Reduced Stochastic Gradient without
Strong Convexity | cs.NA | Stochastic gradient algorithms estimate the gradient based on only one or a
few samples and enjoy low computational cost per iteration. They have been
widely used in large-scale optimization problems. However, stochastic gradient
algorithms are usually slow to converge and achieve sub-linear convergence
rates, due to t... | computer science |
10,476 | Identifying and attacking the saddle point problem in high-dimensional
non-convex optimization | cs.LG | A central challenge to many fields of science and engineering involves
minimizing non-convex error functions over continuous, high dimensional spaces.
Gradient descent or quasi-Newton methods are almost ubiquitously used to
perform such minimizations, and it is often thought that a main source of
difficulty for these l... | computer science |
10,477 | Probabilistic ODE Solvers with Runge-Kutta Means | stat.ML | Runge-Kutta methods are the classic family of solvers for ordinary
differential equations (ODEs), and the basis for the state of the art. Like
most numerical methods, they return point estimates. We construct a family of
probabilistic numerical methods that instead return a Gauss-Markov process
defining a probability d... | computer science |
10,478 | Learning with Cross-Kernels and Ideal PCA | cs.LG | We describe how cross-kernel matrices, that is, kernel matrices between the
data and a custom chosen set of `feature spanning points' can be used for
learning. The main potential of cross-kernels lies in the fact that (a) only
one side of the matrix scales with the number of data points, and (b)
cross-kernels, as oppos... | computer science |
10,479 | Learning Latent Variable Gaussian Graphical Models | stat.ML | Gaussian graphical models (GGM) have been widely used in many
high-dimensional applications ranging from biological and financial data to
recommender systems. Sparsity in GGM plays a central role both statistically
and computationally. Unfortunately, real-world data often does not fit well to
sparse graphical models. I... | computer science |
10,480 | A low variance consistent test of relative dependency | stat.ML | We describe a novel non-parametric statistical hypothesis test of relative
dependence between a source variable and two candidate target variables. Such a
test enables us to determine whether one source variable is significantly more
dependent on a first target variable or a second. Dependence is measured via
the Hilbe... | computer science |
10,481 | The Laplacian K-modes algorithm for clustering | cs.LG | In addition to finding meaningful clusters, centroid-based clustering
algorithms such as K-means or mean-shift should ideally find centroids that are
valid patterns in the input space, representative of data in their cluster.
This is challenging with data having a nonconvex or manifold structure, as with
images or text... | computer science |
10,482 | RAPID: Rapidly Accelerated Proximal Gradient Algorithms for Convex
Minimization | stat.ML | In this paper, we propose a new algorithm to speed-up the convergence of
accelerated proximal gradient (APG) methods. In order to minimize a convex
function $f(\mathbf{x})$, our algorithm introduces a simple line search step
after each proximal gradient step in APG so that a biconvex function
$f(\theta\mathbf{x})$ is m... | computer science |
10,483 | Primitives for Dynamic Big Model Parallelism | stat.ML | When training large machine learning models with many variables or
parameters, a single machine is often inadequate since the model may be too
large to fit in memory, while training can take a long time even with
stochastic updates. A natural recourse is to turn to distributed cluster
computing, in order to harness add... | computer science |
10,484 | Variational Gaussian Process State-Space Models | cs.LG | State-space models have been successfully used for more than fifty years in
different areas of science and engineering. We present a procedure for
efficient variational Bayesian learning of nonlinear state-space models based
on sparse Gaussian processes. The result of learning is a tractable posterior
over nonlinear dy... | computer science |
10,485 | Inferring causal structure: a quantum advantage | cs.LG | The problem of using observed correlations to infer causal relations is
relevant to a wide variety of scientific disciplines. Yet given correlations
between just two classical variables, it is impossible to determine whether
they arose from a causal influence of one on the other or a common cause
influencing both, unle... | computer science |
10,486 | Enhancing Pure-Pixel Identification Performance via Preconditioning | stat.ML | In this paper, we analyze different preconditionings designed to enhance
robustness of pure-pixel search algorithms, which are used for blind
hyperspectral unmixing and which are equivalent to near-separable nonnegative
matrix factorization algorithms. Our analysis focuses on the successive
projection algorithm (SPA), ... | computer science |
10,487 | On semidefinite relaxations for the block model | cs.LG | The stochastic block model (SBM) is a popular tool for community detection in
networks, but fitting it by maximum likelihood (MLE) involves a computationally
infeasible optimization problem. We propose a new semidefinite programming
(SDP) solution to the problem of fitting the SBM, derived as a relaxation of
the MLE. W... | computer science |
10,488 | On the Maximum Entropy Property of the First-Order Stable Spline Kernel
and its Implications | math.ST | A new nonparametric approach for system identification has been recently
proposed where the impulse response is seen as the realization of a zero--mean
Gaussian process whose covariance, the so--called stable spline kernel,
guarantees that the impulse response is almost surely stable. Maximum entropy
properties of the ... | computer science |
10,489 | Convex Optimization Learning of Faithful Euclidean Distance
Representations in Nonlinear Dimensionality Reduction | stat.ML | Classical multidimensional scaling only works well when the noisy distances
observed in a high dimensional space can be faithfully represented by Euclidean
distances in a low dimensional space. Advanced models such as Maximum Variance
Unfolding (MVU) and Minimum Volume Embedding (MVE) use Semi-Definite
Programming (SDP... | computer science |
10,490 | Combining predictions from linear models when training and test inputs
differ | stat.ME | Methods for combining predictions from different models in a supervised
learning setting must somehow estimate/predict the quality of a model's
predictions at unknown future inputs. Many of these methods (often implicitly)
make the assumption that the test inputs are identical to the training inputs,
which is seldom re... | computer science |
10,491 | A scaled gradient projection method for Bayesian learning in dynamical
systems | math.NA | A crucial task in system identification problems is the selection of the most
appropriate model class, and is classically addressed resorting to
cross-validation or using asymptotic arguments. As recently suggested in the
literature, this can be addressed in a Bayesian framework, where model
complexity is regulated by ... | computer science |
10,492 | Mass-Univariate Hypothesis Testing on MEEG Data using Cross-Validation | stat.ML | Recent advances in statistical theory, together with advances in the
computational power of computers, provide alternative methods to do
mass-univariate hypothesis testing in which a large number of univariate tests,
can be properly used to compare MEEG data at a large number of time-frequency
points and scalp location... | computer science |
10,493 | Reconstructing subclonal composition and evolution from whole genome
sequencing of tumors | cs.LG | Tumors often contain multiple subpopulations of cancerous cells defined by
distinct somatic mutations. We describe a new method, PhyloWGS, that can be
applied to WGS data from one or more tumor samples to reconstruct complete
genotypes of these subpopulations based on variant allele frequencies (VAFs) of
point mutation... | computer science |
10,494 | Learning Laplacian Matrix in Smooth Graph Signal Representations | cs.LG | The construction of a meaningful graph plays a crucial role in the success of
many graph-based representations and algorithms for handling structured data,
especially in the emerging field of graph signal processing. However, a
meaningful graph is not always readily available from the data, nor easy to
define depending... | computer science |
10,495 | Simple connectome inference from partial correlation statistics in
calcium imaging | stat.ML | In this work, we propose a simple yet effective solution to the problem of
connectome inference in calcium imaging data. The proposed algorithm consists
of two steps. First, processing the raw signals to detect neural peak
activities. Second, inferring the degree of association between neurons from
partial correlation ... | computer science |
10,496 | Differentially Private Online Learning | cs.LG | In this paper, we consider the problem of preserving privacy in the online
learning setting. We study the problem in the online convex programming (OCP)
framework---a popular online learning setting with several interesting
theoretical and practical implications---while using differential privacy as
the formal privacy ... | computer science |
10,497 | Active Ranking using Pairwise Comparisons | cs.LG | This paper examines the problem of ranking a collection of objects using
pairwise comparisons (rankings of two objects). In general, the ranking of $n$
objects can be identified by standard sorting methods using $n log_2 n$
pairwise comparisons. We are interested in natural situations in which
relationships among the o... | computer science |
10,498 | VC dimension of ellipsoids | math.CO | We will establish that the VC dimension of the class of d-dimensional
ellipsoids is (d^2+3d)/2, and that maximum likelihood estimate with N-component
d-dimensional Gaussian mixture models induces a geometric class having VC
dimension at least N(d^2+3d)/2.
Keywords: VC dimension; finite dimensional ellipsoid; Gaussian... | computer science |
10,499 | Manifold estimation and singular deconvolution under Hausdorff loss | math.ST | We find lower and upper bounds for the risk of estimating a manifold in
Hausdorff distance under several models. We also show that there are close
connections between manifold estimation and the problem of deconvolving a
singular measure. | computer science |
10,500 | Complexity Analysis of the Lasso Regularization Path | stat.ML | The regularization path of the Lasso can be shown to be piecewise linear,
making it possible to "follow" and explicitly compute the entire path. We
analyze in this paper this popular strategy, and prove that its worst case
complexity is exponential in the number of variables. We then oppose this
pessimistic result to a... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.