Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
11,001 | Parallel coordinate descent for the Adaboost problem | cs.LG | We design a randomised parallel version of Adaboost based on previous studies
on parallel coordinate descent. The algorithm uses the fact that the logarithm
of the exponential loss is a function with coordinate-wise Lipschitz continuous
gradient, in order to define the step lengths. We provide the proof of
convergence ... | computer science |
11,002 | Distributed Coordinate Descent Method for Learning with Big Data | stat.ML | In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent method
for solving loss minimization problems with big data. We initially partition
the coordinates (features) and assign each partition to a different node of a
cluster. At every iteration, each node picks a random subset of the coordinates
from tho... | computer science |
11,003 | Semidefinite Programming Based Preconditioning for More Robust
Near-Separable Nonnegative Matrix Factorization | stat.ML | Nonnegative matrix factorization (NMF) under the separability assumption can
provably be solved efficiently, even in the presence of noise, and has been
shown to be a powerful technique in document classification and hyperspectral
unmixing. This problem is referred to as near-separable NMF and requires that
there exist... | computer science |
11,004 | Discriminative Relational Topic Models | cs.LG | Many scientific and engineering fields involve analyzing network data. For
document networks, relational topic models (RTMs) provide a probabilistic
generative process to describe both the link structure and document contents,
and they have shown promise on predicting network structures and discovering
latent topic rep... | computer science |
11,005 | M-Power Regularized Least Squares Regression | stat.ML | Regularization is used to find a solution that both fits the data and is
sufficiently smooth, and thereby is very effective for designing and refining
learning algorithms. But the influence of its exponent remains poorly
understood. In particular, it is unclear how the exponent of the reproducing
kernel Hilbert space~(... | computer science |
11,006 | Gibbs Max-margin Topic Models with Data Augmentation | stat.ML | Max-margin learning is a powerful approach to building classifiers and
structured output predictors. Recent work on max-margin supervised topic models
has successfully integrated it with Bayesian topic models to discover
discriminative latent semantic structures and make accurate predictions for
unseen testing data. Ho... | computer science |
11,007 | Feedback Detection for Live Predictors | stat.ME | A predictor that is deployed in a live production system may perturb the
features it uses to make predictions. Such a feedback loop can occur, for
example, when a model that predicts a certain type of behavior ends up causing
the behavior it predicts, thus creating a self-fulfilling prophecy. In this
paper we analyze p... | computer science |
11,008 | Identifying Influential Entries in a Matrix | cs.NA | For any matrix A in R^(m x n) of rank \rho, we present a probability
distribution over the entries of A (the element-wise leverage scores of
equation (2)) that reveals the most influential entries in the matrix. From a
theoretical perspective, we prove that sampling at most s = O ((m + n) \rho^2
ln (m + n)) entries of ... | computer science |
11,009 | Variance Adjusted Actor Critic Algorithms | stat.ML | We present an actor-critic framework for MDPs where the objective is the
variance-adjusted expected return. Our critic uses linear function
approximation, and we extend the concept of compatible features to the
variance-adjusted setting. We present an episodic actor-critic algorithm and
show that it converges almost su... | computer science |
11,010 | Ridge Fusion in Statistical Learning | stat.ML | We propose a penalized likelihood method to jointly estimate multiple
precision matrices for use in quadratic discriminant analysis and model based
clustering. A ridge penalty and a ridge fusion penalty are used to introduce
shrinkage and promote similarity between precision matrix estimates. Block-wise
coordinate desc... | computer science |
11,011 | Linearized Alternating Direction Method with Parallel Splitting and
Adaptive Penalty for Separable Convex Programs in Machine Learning | cs.NA | Many problems in machine learning and other fields can be (re)for-mulated as
linearly constrained separable convex programs. In most of the cases, there are
multiple blocks of variables. However, the traditional alternating direction
method (ADM) and its linearized version (LADM, obtained by linearizing the
quadratic p... | computer science |
11,012 | MLI: An API for Distributed Machine Learning | cs.LG | MLI is an Application Programming Interface designed to address the
challenges of building Machine Learn- ing algorithms in a distributed setting
based on data-centric computing. Its primary goal is to simplify the
development of high-performance, scalable, distributed algorithms. Our initial
results show that, relativ... | computer science |
11,013 | Randomized co-training: from cortical neurons to machine learning and
back again | cs.LG | Despite its size and complexity, the human cortex exhibits striking
anatomical regularities, suggesting there may simple meta-algorithms underlying
cortical learning and computation. We expect such meta-algorithms to be of
interest since they need to operate quickly, scalably and effectively with
little-to-no specializ... | computer science |
11,014 | Predicting the NFL using Twitter | cs.SI | We study the relationship between social media output and National Football
League (NFL) games, using a dataset containing messages from Twitter and NFL
game statistics. Specifically, we consider tweets pertaining to specific teams
and games in the NFL season and use them alongside statistical game data to
build predic... | computer science |
11,015 | Relax but stay in control: from value to algorithms for online Markov
decision processes | cs.LG | Online learning algorithms are designed to perform in non-stationary
environments, but generally there is no notion of a dynamic state to model
constraints on current and future actions as a function of past actions.
State-based models are common in stochastic control settings, but commonly used
frameworks such as Mark... | computer science |
11,016 | Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind
Source Separation | stat.ML | In this paper, we propose a new fast and robust recursive algorithm for
near-separable nonnegative matrix factorization, a particular nonnegative blind
source separation problem. This algorithm, which we refer to as the successive
nonnegative projection algorithm (SNPA), is closely related to the popular
successive pro... | computer science |
11,017 | Automatic Classification of Variable Stars in Catalogs with missing data | cs.LG | We present an automatic classification method for astronomical catalogs with
missing data. We use Bayesian networks, a probabilistic graphical model, that
allows us to perform inference to pre- dict missing values given observed data
and dependency relationships between variables. To learn a Bayesian network
from incom... | computer science |
11,018 | Learning Sparsely Used Overcomplete Dictionaries via Alternating
Minimization | cs.LG | We consider the problem of sparse coding, where each sample consists of a
sparse linear combination of a set of dictionary atoms, and the task is to
learn both the dictionary elements and the mixing coefficients. Alternating
minimization is a popular heuristic for sparse coding, where the dictionary and
the coefficient... | computer science |
11,019 | Necessary and Sufficient Conditions for Novel Word Detection in
Separable Topic Models | cs.LG | The simplicial condition and other stronger conditions that imply it have
recently played a central role in developing polynomial time algorithms with
provable asymptotic consistency and sample complexity guarantees for topic
estimation in separable topic models. Of these algorithms, those that rely
solely on the simpl... | computer science |
11,020 | Statistical Inference in Hidden Markov Models using $k$-segment
Constraints | stat.ME | Hidden Markov models (HMMs) are one of the most widely used statistical
methods for analyzing sequence data. However, the reporting of output from HMMs
has largely been restricted to the presentation of the most-probable (MAP)
hidden state sequence, found via the Viterbi algorithm, or the sequence of most
probable marg... | computer science |
11,021 | The Maximum Entropy Relaxation Path | cs.LG | The relaxed maximum entropy problem is concerned with finding a probability
distribution on a finite set that minimizes the relative entropy to a given
prior distribution, while satisfying relaxed max-norm constraints with respect
to a third observed multinomial distribution. We study the entire relaxation
path for thi... | computer science |
11,022 | Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse
Signals | cs.IT | We consider the problem of recovering block-sparse signals whose structures
are unknown \emph{a priori}. Block-sparse signals with nonzero coefficients
occurring in clusters arise naturally in many practical scenarios. However, the
knowledge of the block structure is usually unavailable in practice. In this
paper, we d... | computer science |
11,023 | FuSSO: Functional Shrinkage and Selection Operator | stat.ML | We present the FuSSO, a functional analogue to the LASSO, that efficiently
finds a sparse set of functional input covariates to regress a real-valued
response against. The FuSSO does so in a semi-parametric fashion, making no
parametric assumptions about the nature of input functional covariates and
assuming a linear f... | computer science |
11,024 | Fast Distribution To Real Regression | stat.ML | We study the problem of distribution to real-value regression, where one aims
to regress a mapping $f$ that takes in a distribution input covariate $P\in
\mathcal{I}$ (for a non-parametric family of distributions $\mathcal{I}$) and
outputs a real-valued response $Y=f(P) + \epsilon$. This setting was recently
studied, a... | computer science |
11,025 | Global Sensitivity Analysis with Dependence Measures | math.ST | Global sensitivity analysis with variance-based measures suffers from several
theoretical and practical limitations, since they focus only on the variance of
the output and handle multivariate variables in a limited way. In this paper,
we introduce a new class of sensitivity indices based on dependence measures
which o... | computer science |
11,026 | DinTucker: Scaling up Gaussian process models on multidimensional arrays
with billions of elements | cs.LG | Infinite Tucker Decomposition (InfTucker) and random function prior models,
as nonparametric Bayesian models on infinite exchangeable arrays, are more
powerful models than widely-used multilinear factorization methods including
Tucker and PARAFAC decomposition, (partly) due to their capability of modeling
nonlinear rel... | computer science |
11,027 | Reinforcement Learning for Matrix Computations: PageRank as an Example | cs.LG | Reinforcement learning has gained wide popularity as a technique for
simulation-driven approximate dynamic programming. A less known aspect is that
the very reasons that make it effective in dynamic programming can also be
leveraged for using it for distributed schemes for certain matrix computations
involving non-nega... | computer science |
11,028 | The More, the Merrier: the Blessing of Dimensionality for Learning Large
Gaussian Mixtures | cs.LG | In this paper we show that very large mixtures of Gaussians are efficiently
learnable in high dimension. More precisely, we prove that a mixture with known
identical covariance matrices whose number of components is a polynomial of any
fixed degree in the dimension n is polynomially learnable as long as a certain
non-d... | computer science |
11,029 | Approximate Inference in Continuous Determinantal Point Processes | stat.ML | Determinantal point processes (DPPs) are random point processes well-suited
for modeling repulsion. In machine learning, the focus of DPP-based models has
been on diverse subset selection from a discrete and finite base set. This
discrete setting admits an efficient sampling algorithm based on the
eigendecomposition of... | computer science |
11,030 | Smoothed Analysis of Tensor Decompositions | cs.DS | Low rank tensor decompositions are a powerful tool for learning generative
models, and uniqueness results give them a significant advantage over matrix
decomposition methods. However, tensors pose significant algorithmic challenges
and tensors analogs of much of the matrix algebra toolkit are unlikely to exist
because ... | computer science |
11,031 | Mapping cognitive ontologies to and from the brain | stat.ML | Imaging neuroscience links brain activation maps to behavior and cognition
via correlational studies. Due to the nature of the individual experiments,
based on eliciting neural response from a small number of stimuli, this link is
incomplete, and unidirectional from the causal point of view. To come to
conclusions on t... | computer science |
11,032 | Towards Big Topic Modeling | cs.LG | To solve the big topic modeling problem, we need to reduce both time and
space complexities of batch latent Dirichlet allocation (LDA) algorithms.
Although parallel LDA algorithms on the multi-processor architecture have low
time and space complexities, their communication costs among processors often
scale linearly wi... | computer science |
11,033 | Stochastic processes and feedback-linearisation for online
identification and Bayesian adaptive control of fully-actuated mechanical
systems | cs.LG | This work proposes a new method for simultaneous probabilistic identification
and control of an observable, fully-actuated mechanical system. Identification
is achieved by conditioning stochastic process priors on observations of
configurations and noisy estimates of configuration derivatives. In contrast to
previous w... | computer science |
11,034 | Asymptotically Exact, Embarrassingly Parallel MCMC | stat.ML | Communication costs, resulting from synchronization requirements during
learning, can greatly slow down many parallel machine learning algorithms. In
this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in
which subsets of data are processed independently, with very little
communication. First, w... | computer science |
11,035 | Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization | cs.LG | Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedure
for finding sparse solutions of underdetermined linear systems. This method has
been shown to have strong theoretical guarantee and impressive numerical
performance. In this paper, we generalize HTP from compressive sensing to a
generic problem ... | computer science |
11,036 | Off-policy reinforcement learning for $ H_\infty $ control design | cs.SY | The $H_\infty$ control design problem is considered for nonlinear systems
with unknown internal system model. It is known that the nonlinear $ H_\infty $
control problem can be transformed into solving the so-called
Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial
differential equation that is genera... | computer science |
11,037 | Learning Reputation in an Authorship Network | cs.SI | The problem of searching for experts in a given academic field is hugely
important in both industry and academia. We study exactly this issue with
respect to a database of authors and their publications. The idea is to use
Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform
topic modelling i... | computer science |
11,038 | Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching | math.OC | Graph matching is a challenging problem with very important applications in a
wide range of fields, from image and video analysis to biological and
biomedical problems. We propose a robust graph matching algorithm inspired in
sparsity-related techniques. We cast the problem, resembling group or
collaborative sparsity f... | computer science |
11,039 | Practical Inexact Proximal Quasi-Newton Method with Global Complexity
Analysis | cs.LG | Recently several methods were proposed for sparse optimization which make
careful use of second-order information [10, 28, 16, 3] to improve local
convergence rates. These methods construct a composite quadratic approximation
using Hessian information, optimize this approximation using a first-order
method, such as coo... | computer science |
11,040 | Dimensionality reduction for click-through rate prediction: Dense versus
sparse representation | stat.ML | In online advertising, display ads are increasingly being placed based on
real-time auctions where the advertiser who wins gets to serve the ad. This is
called real-time bidding (RTB). In RTB, auctions have very tight time
constraints on the order of 100ms. Therefore mechanisms for bidding
intelligently such as clickth... | computer science |
11,041 | ADMM Algorithm for Graphical Lasso with an $\ell_{\infty}$ Element-wise
Norm Constraint | cs.LG | We consider the problem of Graphical lasso with an additional $\ell_{\infty}$
element-wise norm constraint on the precision matrix. This problem has
applications in high-dimensional covariance decomposition such as in
\citep{Janzamin-12}. We propose an ADMM algorithm to solve this problem. We
also use a continuation st... | computer science |
11,042 | Stochastic continuum armed bandit problem of few linear parameters in
high dimensions | stat.ML | We consider a stochastic continuum armed bandit problem where the arms are
indexed by the $\ell_2$ ball $B_{d}(1+\nu)$ of radius $1+\nu$ in
$\mathbb{R}^d$. The reward functions $r :B_{d}(1+\nu) \rightarrow \mathbb{R}$
are considered to intrinsically depend on $k \ll d$ unknown linear parameters
so that $r(\mathbf{x}) =... | computer science |
11,043 | Consistency of weighted majority votes | math.PR | We revisit the classical decision-theoretic problem of weighted expert voting
from a statistical learning perspective. In particular, we examine the
consistency (both asymptotic and finitary) of the optimal Nitzan-Paroush
weighted majority and related rules. In the case of known expert competence
levels, we give sharp ... | computer science |
11,044 | Grid Topology Identification using Electricity Prices | cs.LG | The potential of recovering the topology of a grid using solely publicly
available market data is explored here. In contemporary whole-sale electricity
markets, real-time prices are typically determined by solving the
network-constrained economic dispatch problem. Under a linear DC model,
locational marginal prices (LM... | computer science |
11,045 | Understanding Alternating Minimization for Matrix Completion | cs.LG | Alternating Minimization is a widely used and empirically successful
heuristic for matrix completion and related low-rank optimization problems.
Theoretical guarantees for Alternating Minimization have been hard to come by
and are still poorly understood. This is in part because the heuristic is
iterative and non-conve... | computer science |
11,046 | Max-Min Distance Nonnegative Matrix Factorization | stat.ML | Nonnegative Matrix Factorization (NMF) has been a popular representation
method for pattern classification problem. It tries to decompose a nonnegative
matrix of data samples as the product of a nonnegative basic matrix and a
nonnegative coefficient matrix, and the coefficient matrix is used as the new
representation. ... | computer science |
11,047 | Robust Subspace System Identification via Weighted Nuclear Norm
Optimization | cs.SY | Subspace identification is a classical and very well studied problem in
system identification. The problem was recently posed as a convex optimization
problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this
framework to handle outliers. The proposed framework takes the form of a convex
optimizat... | computer science |
11,048 | Sequential Monte Carlo Inference of Mixed Membership Stochastic
Blockmodels for Dynamic Social Networks | cs.SI | Many kinds of data can be represented as a network or graph. It is crucial to
infer the latent structure underlying such a network and to predict unobserved
links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a
promising model for network data. Latent variables and unknown parameters in
MMSB have bee... | computer science |
11,049 | Budgeted Influence Maximization for Multiple Products | cs.LG | The typical algorithmic problem in viral marketing aims to identify a set of
influential users in a social network, who, when convinced to adopt a product,
shall influence other users in the network and trigger a large cascade of
adoptions. However, the host (the owner of an online social platform) often
faces more con... | computer science |
11,050 | Parametric Modelling of Multivariate Count Data Using Probabilistic
Graphical Models | stat.ML | Multivariate count data are defined as the number of items of different
categories issued from sampling within a population, which individuals are
grouped into categories. The analysis of multivariate count data is a recurrent
and crucial issue in numerous modelling problems, particularly in the fields of
biology and e... | computer science |
11,051 | Contextually Supervised Source Separation with Application to Energy
Disaggregation | stat.ML | We propose a new framework for single-channel source separation that lies
between the fully supervised and unsupervised setting. Instead of supervision,
we provide input features for each source signal and use convex methods to
estimate the correlations between these features and the unobserved signal
decomposition. We... | computer science |
11,052 | Permuted NMF: A Simple Algorithm Intended to Minimize the Volume of the
Score Matrix | stat.AP | Non-Negative Matrix Factorization, NMF, attempts to find a number of
archetypal response profiles, or parts, such that any sample profile in the
dataset can be approximated by a close profile among these archetypes or a
linear combination of these profiles. The non-negativity constraint is imposed
while estimating arch... | computer science |
11,053 | The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited | stat.ML | Hypergraphs allow one to encode higher-order relationships in data and are
thus a very flexible modeling tool. Current learning methods are either based
on approximations of the hypergraphs via graphs or on tensor methods which are
only applicable under special conditions. In this paper, we present a new
learning frame... | computer science |
11,054 | Nonlinear Eigenproblems in Data Analysis - Balanced Graph Cuts and the
RatioDCA-Prox | stat.ML | It has been recently shown that a large class of balanced graph cuts allows
for an exact relaxation into a nonlinear eigenproblem. We review briefly some
of these results and propose a family of algorithms to compute nonlinear
eigenvectors which encompasses previous work as special cases. We provide a
detailed analysis... | computer science |
11,055 | Time-varying Learning and Content Analytics via Sparse Factor Analysis | stat.ML | We propose SPARFA-Trace, a new machine learning-based framework for
time-varying learning and content analytics for education applications. We
develop a novel message passing-based, blind, approximate Kalman filter for
sparse factor analysis (SPARFA), that jointly (i) traces learner concept
knowledge over time, (ii) an... | computer science |
11,056 | SOMz: photometric redshift PDFs with self organizing maps and random
atlas | cs.LG | In this paper we explore the applicability of the unsupervised machine
learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric
redshift probability density functions (PDFs). This technique takes a
spectroscopic training set, and maps the photometric attributes, but not the
redshifts, to a two di... | computer science |
11,057 | Large-Scale Paralleled Sparse Principal Component Analysis | cs.MS | Principal component analysis (PCA) is a statistical technique commonly used
in multivariate data analysis. However, PCA can be difficult to interpret and
explain since the principal components (PCs) are linear combinations of the
original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and
interpretab... | computer science |
11,058 | Parallel architectures for fuzzy triadic similarity learning | cs.DC | In a context of document co-clustering, we define a new similarity measure
which iteratively computes similarity while combining fuzzy sets in a
three-partite graph. The fuzzy triadic similarity (FT-Sim) model can deal with
uncertainty offers by the fuzzy sets. Moreover, with the development of the Web
and the high ava... | computer science |
11,059 | Using Latent Binary Variables for Online Reconstruction of Large Scale
Systems | math.PR | We propose a probabilistic graphical model realizing a minimal encoding of
real variables dependencies based on possibly incomplete observation and an
empirical cumulative distribution function per variable. The target application
is a large scale partially observed system, like e.g. a traffic network, where
a small pr... | computer science |
11,060 | A Fast Greedy Algorithm for Generalized Column Subset Selection | cs.DS | This paper defines a generalized column subset selection problem which is
concerned with the selection of a few columns from a source matrix A that best
approximate the span of a target matrix B. The paper then proposes a fast
greedy algorithm for solving this problem and draws connections to different
problems that ca... | computer science |
11,061 | A hidden process regression model for functional data description.
Application to curve discrimination | stat.ME | A new approach for functional data description is proposed in this paper. It
consists of a regression model with a discrete hidden logistic process which is
adapted for modeling curves with abrupt or smooth regime changes. The model
parameters are estimated in a maximum likelihood framework through a dedicated
Expectat... | computer science |
11,062 | A regression model with a hidden logistic process for signal
parametrization | stat.ME | A new approach for signal parametrization, which consists of a specific
regression model incorporating a discrete hidden logistic process, is proposed.
The model parameters are estimated by the maximum likelihood method performed
by a dedicated Expectation Maximization (EM) algorithm. The parameters of the
hidden logis... | computer science |
11,063 | Supervised learning of a regression model based on latent process.
Application to the estimation of fuel cell life time | stat.ML | This paper describes a pattern recognition approach aiming to estimate fuel
cell duration time from electrochemical impedance spectroscopy measurements. It
consists in first extracting features from both real and imaginary parts of the
impedance spectrum. A parametric model is considered in the case of the real
part, w... | computer science |
11,064 | A Convex Formulation for Mixed Regression with Two Components: Minimax
Optimal Rates | stat.ML | We consider the mixed regression problem with two components, under
adversarial and stochastic noise. We give a convex optimization formulation
that provably recovers the true solution, and provide upper bounds on the
recovery errors for both arbitrary noise and stochastic noise settings. We also
give matching minimax ... | computer science |
11,065 | Functional Mixture Discriminant Analysis with hidden process regression
for curve classification | stat.ME | We present a new mixture model-based discriminant analysis approach for
functional data using a specific hidden process regression model. The approach
allows for fitting flexible curve-models to each class of complex-shaped curves
presenting regime changes. The model parameters are learned by maximizing the
observed-da... | computer science |
11,066 | Mixture model-based functional discriminant analysis for curve
classification | stat.ME | Statistical approaches for Functional Data Analysis concern the paradigm for
which the individuals are functions or curves rather than finite dimensional
vectors. In this paper, we particularly focus on the modeling and the
classification of functional data which are temporal curves presenting regime
changes over time.... | computer science |
11,067 | Robust EM algorithm for model-based curve clustering | stat.ME | Model-based clustering approaches concern the paradigm of exploratory data
analysis relying on the finite mixture model to automatically find a latent
structure governing observed data. They are one of the most popular and
successful approaches in cluster analysis. The mixture density estimation is
generally performed ... | computer science |
11,068 | Model-based clustering with Hidden Markov Model regression for time
series with regime changes | stat.ML | This paper introduces a novel model-based clustering approach for clustering
time series which present changes in regime. It consists of a mixture of
polynomial regressions governed by hidden Markov chains. The underlying hidden
process for each cluster activates successively several polynomial regimes
during time. The... | computer science |
11,069 | Active Discovery of Network Roles for Predicting the Classes of Network
Nodes | cs.LG | Nodes in real world networks often have class labels, or underlying
attributes, that are related to the way in which they connect to other nodes.
Sometimes this relationship is simple, for instance nodes of the same class are
may be more likely to be connected. In other cases, however, this is not true,
and the way tha... | computer science |
11,070 | Petuum: A New Platform for Distributed Machine Learning on Big Data | stat.ML | What is a systematic way to efficiently apply a wide spectrum of advanced ML
programs to industrial scale problems, using Big Models (up to 100s of billions
of parameters) on Big Data (up to terabytes or petabytes)? Modern
parallelization strategies employ fine-grained operations and scheduling beyond
the classic bulk-... | computer science |
11,071 | Communication Efficient Distributed Optimization using an Approximate
Newton-type Method | cs.LG | We present a novel Newton-type method for distributed optimization, which is
particularly well suited for stochastic optimization and learning problems. For
quadratic objectives, the method enjoys a linear rate of convergence which
provably \emph{improves} with the data size, requiring an essentially constant
number of... | computer science |
11,072 | Consistent Bounded-Asynchronous Parameter Servers for Distributed ML | stat.ML | In distributed ML applications, shared parameters are usually replicated
among computing nodes to minimize network overhead. Therefore, proper
consistency model must be carefully chosen to ensure algorithm's correctness
and provide high throughput. Existing consistency models used in
general-purpose databases and moder... | computer science |
11,073 | A Deep Representation for Invariance And Music Classification | cs.SD | Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminativ... | computer science |
11,074 | Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side
Information | cs.NI | In this paper, we address the problem of reconstructing coverage maps from
path-loss measurements in cellular networks. We propose and evaluate two
kernel-based adaptive online algorithms as an alternative to typical offline
methods. The proposed algorithms are application-tailored extensions of
powerful iterative meth... | computer science |
11,075 | Understanding Machine-learned Density Functionals | cs.LG | Kernel ridge regression is used to approximate the kinetic energy of
non-interacting fermions in a one-dimensional box as a functional of their
density. The properties of different kernels and methods of cross-validation
are explored, and highly accurate energies are achieved. Accurate {\em
constrained optimal densitie... | computer science |
11,076 | Optimal learning with Bernstein Online Aggregation | stat.ML | We introduce a new recursive aggregation procedure called Bernstein Online
Aggregation (BOA). The exponential weights include an accuracy term and a
second order term that is a proxy of the quadratic variation as in Hazan and
Kale (2010). This second term stabilizes the procedure that is optimal in
different senses. We... | computer science |
11,077 | Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion | cs.LG | In this paper, we propose an efficient and scalable low rank matrix
completion algorithm. The key idea is to extend orthogonal matching pursuit
method from the vector case to the matrix case. We further propose an economic
version of our algorithm by introducing a novel weight updating rule to reduce
the time and stora... | computer science |
11,078 | A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse
Learning | cs.DC | Learning sparse combinations is a frequent theme in machine learning. In this
paper, we study its associated optimization problem in the distributed setting
where the elements to be combined are not centrally located but spread over a
network. We address the key challenges of balancing communication costs and
optimizat... | computer science |
11,079 | Open problem: Tightness of maximum likelihood semidefinite relaxations | math.OC | We have observed an interesting, yet unexplained, phenomenon: Semidefinite
programming (SDP) based relaxations of maximum likelihood estimators (MLE) tend
to be tight in recovery problems with noisy data, even when MLE cannot exactly
recover the ground truth. Several results establish tightness of SDP based
relaxations... | computer science |
11,080 | Anytime Hierarchical Clustering | stat.ML | We propose a new anytime hierarchical clustering method that iteratively
transforms an arbitrary initial hierarchy on the configuration of measurements
along a sequence of trees we prove for a fixed data set must terminate in a
chain of nested partitions that satisfies a natural homogeneity requirement.
Each recursive ... | computer science |
11,081 | Hybrid Conditional Gradient - Smoothing Algorithms with Applications to
Sparse and Low Rank Regularization | math.OC | We study a hybrid conditional gradient - smoothing algorithm (HCGS) for
solving composite convex optimization problems which contain several terms over
a bounded set. Examples of these include regularization problems with several
norms as penalties and a norm constraint. HCGS extends conditional gradient
methods to cas... | computer science |
11,082 | MEG Decoding Across Subjects | stat.ML | Brain decoding is a data analysis paradigm for neuroimaging experiments that
is based on predicting the stimulus presented to the subject from the
concurrent brain activity. In order to make inference at the group level, a
straightforward but sometimes unsuccessful approach is to train a classifier on
the trials of a g... | computer science |
11,083 | A New Space for Comparing Graphs | stat.ME | Finding a new mathematical representations for graph, which allows direct
comparison between different graph structures, is an open-ended research
direction. Having such a representation is the first prerequisite for a variety
of machine learning algorithms like classification, clustering, etc., over
graph datasets. In... | computer science |
11,084 | Subspace Learning and Imputation for Streaming Big Data Matrices and
Tensors | stat.ML | Extracting latent low-dimensional structure from high-dimensional data is of
paramount importance in timely inference tasks encountered with `Big Data'
analytics. However, increasingly noisy, heterogeneous, and incomplete datasets
as well as the need for {\em real-time} processing of streaming data pose major
challenge... | computer science |
11,085 | Tight bounds for learning a mixture of two gaussians | cs.LG | We consider the problem of identifying the parameters of an unknown mixture
of two arbitrary $d$-dimensional gaussians from a sequence of independent
random samples. Our main results are upper and lower bounds giving a
computationally efficient moment-based estimator with an optimal convergence
rate, thus resolving a p... | computer science |
11,086 | Spatiotemporal Sparse Bayesian Learning with Applications to Compressed
Sensing of Multichannel Physiological Signals | cs.IT | Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic... | computer science |
11,087 | GP-Localize: Persistent Mobile Robot Localization using Online Sparse
Gaussian Process Observation Model | cs.RO | Central to robot exploration and mapping is the task of persistent
localization in environmental fields characterized by spatially correlated
measurements. This paper presents a Gaussian process localization (GP-Localize)
algorithm that, in contrast to existing works, can exploit the spatially
correlated field measurem... | computer science |
11,088 | Forward - Backward Greedy Algorithms for Atomic Norm Regularization | cs.DS | In many signal processing applications, the aim is to reconstruct a signal
that has a simple representation with respect to a certain basis or frame.
Fundamental elements of the basis known as "atoms" allow us to define "atomic
norms" that can be used to formulate convex regularizations for the
reconstruction problem. ... | computer science |
11,089 | CoRE Kernels | stat.ML | The term "CoRE kernel" stands for correlation-resemblance kernel. In many
applications (e.g., vision), the data are often high-dimensional, sparse, and
non-binary. We propose two types of (nonlinear) CoRE kernels for non-binary
sparse data and demonstrate the effectiveness of the new kernels through a
classification ex... | computer science |
11,090 | Multiscale Event Detection in Social Media | cs.SI | Event detection has been one of the most important research topics in social
media analysis. Most of the traditional approaches detect events based on fixed
temporal and spatial resolutions, while in reality events of different scales
usually occur simultaneously, namely, they span different intervals in time and
space... | computer science |
11,091 | Automatic Differentiation of Algorithms for Machine Learning | cs.LG | Automatic differentiation---the mechanical transformation of numeric computer
programs to calculate derivatives efficiently and accurately---dates to the
origin of the computer age. Reverse mode automatic differentiation both
antedates and generalizes the method of backwards propagation of errors used in
machine learni... | computer science |
11,092 | Majority Vote of Diverse Classifiers for Late Fusion | stat.ML | In the past few years, a lot of attention has been devoted to multimedia
indexing by fusing multimodal informations. Two kinds of fusion schemes are
generally considered: The early fusion and the late fusion. We focus on late
classifier fusion, where one combines the scores of each modality at the
decision level. To ta... | computer science |
11,093 | Rates of Convergence for Nearest Neighbor Classification | cs.LG | Nearest neighbor methods are a popular class of nonparametric estimators with
several desirable properties, such as adaptivity to different distance scales
in different regions of space. Prior work on convergence rates for nearest
neighbor classification has not fully reflected these subtle properties. We
analyze the b... | computer science |
11,094 | SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly
Convex Composite Objectives | cs.LG | In this work we introduce a new optimisation method called SAGA in the spirit
of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient
algorithms with fast linear convergence rates. SAGA improves on the theory
behind SAG and SVRG, with better theoretical convergence rates, and has support
for compos... | computer science |
11,095 | DC approximation approaches for sparse optimization | cs.NA | Sparse optimization refers to an optimization problem involving the zero-norm
in objective or constraints. In this paper, nonconvex approximation approaches
for sparse optimization have been studied with a unifying point of view in DC
(Difference of Convex functions) programming framework. Considering a common DC
appro... | computer science |
11,096 | Identifying Outliers in Large Matrices via Randomized Adaptive
Compressive Sampling | cs.IT | This paper examines the problem of locating outlier columns in a large,
otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and
inference approach and establish theoretical guarantees for its performance;
our results show that accurate outlier identification is achievable using very
few linear sum... | computer science |
11,097 | Significant Subgraph Mining with Multiple Testing Correction | stat.ME | The problem of finding itemsets that are statistically significantly enriched
in a class of transactions is complicated by the need to correct for multiple
hypothesis testing. Pruning untestable hypotheses was recently proposed as a
strategy for this task of significant itemset mining. It was shown to lead to
greater s... | computer science |
11,098 | Classification-based Approximate Policy Iteration: Experiments and
Extended Discussions | cs.LG | Tackling large approximate dynamic programming or reinforcement learning
problems requires methods that can exploit regularities, or intrinsic
structure, of the problem in hand. Most current methods are geared towards
exploiting the regularities of either the value function or the policy. We
introduce a general classif... | computer science |
11,099 | Fast Algorithm for Low-rank matrix recovery in Poisson noise | stat.ML | This paper describes a fast algorithm for recovering low-rank matrices from
their linear measurements contaminated with Poisson noise: the Poisson noise
Maximum Likelihood Singular Value thresholding (PMLSV) algorithm. We propose a
convex optimization formulation with a cost function consisting of the sum of a
likeliho... | computer science |
11,100 | Global convergence of splitting methods for nonconvex composite
optimization | math.OC | We consider the problem of minimizing the sum of a smooth function $h$ with a
bounded Hessian, and a nonsmooth function. We assume that the latter function
is a composition of a proper closed function $P$ and a surjective linear map
$\cal M$, with the proximal mappings of $\tau P$, $\tau > 0$, simple to
compute. This p... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.