Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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11,101 | Reducing Offline Evaluation Bias in Recommendation Systems | cs.IR | Recommendation systems have been integrated into the majority of large online
systems. They tailor those systems to individual users by filtering and ranking
information according to user profiles. This adaptation process influences the
way users interact with the system and, as a consequence, increases the
difficulty ... | computer science |
11,102 | Robust Optimization using Machine Learning for Uncertainty Sets | math.OC | Our goal is to build robust optimization problems for making decisions based
on complex data from the past. In robust optimization (RO) generally, the goal
is to create a policy for decision-making that is robust to our uncertainty
about the future. In particular, we want our policy to best handle the the
worst possibl... | computer science |
11,103 | Dictionary Learning and Tensor Decomposition via the Sum-of-Squares
Method | cs.DS | We give a new approach to the dictionary learning (also known as "sparse
coding") problem of recovering an unknown $n\times m$ matrix $A$ (for $m \geq
n$) from examples of the form \[ y = Ax + e, \] where $x$ is a random vector in
$\mathbb R^m$ with at most $\tau m$ nonzero coordinates, and $e$ is a random
noise vector... | computer science |
11,104 | Identifying Cover Songs Using Information-Theoretic Measures of
Similarity | cs.IR | This paper investigates methods for quantifying similarity between audio
signals, specifically for the task of of cover song detection. We consider an
information-theoretic approach, where we compute pairwise measures of
predictability between time series. We compare discrete-valued approaches
operating on quantised au... | computer science |
11,105 | Private Learning and Sanitization: Pure vs. Approximate Differential
Privacy | cs.LG | We compare the sample complexity of private learning [Kasiviswanathan et al.
2008] and sanitization~[Blum et al. 2008] under pure $\epsilon$-differential
privacy [Dwork et al. TCC 2006] and approximate
$(\epsilon,\delta)$-differential privacy [Dwork et al. Eurocrypt 2006]. We show
that the sample complexity of these ta... | computer science |
11,106 | Bandits Warm-up Cold Recommender Systems | cs.LG | We address the cold start problem in recommendation systems assuming no
contextual information is available neither about users, nor items. We consider
the case in which we only have access to a set of ratings of items by users.
Most of the existing works consider a batch setting, and use cross-validation
to tune param... | computer science |
11,107 | Density Adaptive Parallel Clustering | cs.DS | In this paper we are going to introduce a new nearest neighbours based
approach to clustering, and compare it with previous solutions; the resulting
algorithm, which takes inspiration from both DBscan and minimum spanning tree
approaches, is deterministic but proves simpler, faster and doesnt require to
set in advance ... | computer science |
11,108 | Altitude Training: Strong Bounds for Single-Layer Dropout | stat.ML | Dropout training, originally designed for deep neural networks, has been
successful on high-dimensional single-layer natural language tasks. This paper
proposes a theoretical explanation for this phenomenon: we show that, under a
generative Poisson topic model with long documents, dropout training improves
the exponent... | computer science |
11,109 | Fast matrix completion without the condition number | cs.LG | We give the first algorithm for Matrix Completion whose running time and
sample complexity is polynomial in the rank of the unknown target matrix,
linear in the dimension of the matrix, and logarithmic in the condition number
of the matrix. To the best of our knowledge, all previous algorithms either
incurred a quadrat... | computer science |
11,110 | Sequential Logistic Principal Component Analysis (SLPCA): Dimensional
Reduction in Streaming Multivariate Binary-State System | stat.ML | Sequential or online dimensional reduction is of interests due to the
explosion of streaming data based applications and the requirement of adaptive
statistical modeling, in many emerging fields, such as the modeling of energy
end-use profile. Principal Component Analysis (PCA), is the classical way of
dimensional redu... | computer science |
11,111 | Sparse Partially Linear Additive Models | stat.ME | The generalized partially linear additive model (GPLAM) is a flexible and
interpretable approach to building predictive models. It combines features in
an additive manner, allowing each to have either a linear or nonlinear effect
on the response. However, the choice of which features to treat as linear or
nonlinear is ... | computer science |
11,112 | Tight convex relaxations for sparse matrix factorization | stat.ML | Based on a new atomic norm, we propose a new convex formulation for sparse
matrix factorization problems in which the number of nonzero elements of the
factors is assumed fixed and known. The formulation counts sparse PCA with
multiple factors, subspace clustering and low-rank sparse bilinear regression
as potential ap... | computer science |
11,113 | Learning Rank Functionals: An Empirical Study | cs.IR | Ranking is a key aspect of many applications, such as information retrieval,
question answering, ad placement and recommender systems. Learning to rank has
the goal of estimating a ranking model automatically from training data. In
practical settings, the task often reduces to estimating a rank functional of
an object ... | computer science |
11,114 | Permutation Models for Collaborative Ranking | cs.IR | We study the problem of collaborative filtering where ranking information is
available. Focusing on the core of the collaborative ranking process, the user
and their community, we propose new models for representation of the underlying
permutations and prediction of ranks. The first approach is based on the
assumption ... | computer science |
11,115 | Algorithms, Initializations, and Convergence for the Nonnegative Matrix
Factorization | cs.NA | It is well known that good initializations can improve the speed and accuracy
of the solutions of many nonnegative matrix factorization (NMF) algorithms.
Many NMF algorithms are sensitive with respect to the initialization of W or H
or both. This is especially true of algorithms of the alternating least squares
(ALS) t... | computer science |
11,116 | Dependence versus Conditional Dependence in Local Causal Discovery from
Gene Expression Data | cs.LG | Motivation: Algorithms that discover variables which are causally related to
a target may inform the design of experiments. With observational gene
expression data, many methods discover causal variables by measuring each
variable's degree of statistical dependence with the target using dependence
measures (DMs). Howev... | computer science |
11,117 | Differentially-Private Logistic Regression for Detecting Multiple-SNP
Association in GWAS Databases | stat.ML | Following the publication of an attack on genome-wide association studies
(GWAS) data proposed by Homer et al., considerable attention has been given to
developing methods for releasing GWAS data in a privacy-preserving way. Here,
we develop an end-to-end differentially private method for solving regression
problems wi... | computer science |
11,118 | Fast Bayesian Feature Selection for High Dimensional Linear Regression
in Genomics via the Ising Approximation | cs.LG | Feature selection, identifying a subset of variables that are relevant for
predicting a response, is an important and challenging component of many
methods in statistics and machine learning. Feature selection is especially
difficult and computationally intensive when the number of variables approaches
or exceeds the n... | computer science |
11,119 | A Plug&Play P300 BCI Using Information Geometry | cs.LG | This paper presents a new classification methods for Event Related Potentials
(ERP) based on an Information geometry framework. Through a new estimation of
covariance matrices, this work extend the use of Riemannian geometry, which was
previously limited to SMR-based BCI, to the problem of classification of ERPs.
As co... | computer science |
11,120 | High-performance Kernel Machines with Implicit Distributed Optimization
and Randomization | stat.ML | In order to fully utilize "big data", it is often required to use "big
models". Such models tend to grow with the complexity and size of the training
data, and do not make strong parametric assumptions upfront on the nature of
the underlying statistical dependencies. Kernel methods fit this need well, as
they constitut... | computer science |
11,121 | Communication-Efficient Distributed Dual Coordinate Ascent | cs.LG | Communication remains the most significant bottleneck in the performance of
distributed optimization algorithms for large-scale machine learning. In this
paper, we propose a communication-efficient framework, CoCoA, that uses local
computation in a primal-dual setting to dramatically reduce the amount of
necessary comm... | computer science |
11,122 | Machine Learning Etudes in Astrophysics: Selection Functions for Mock
Cluster Catalogs | cs.LG | Making mock simulated catalogs is an important component of astrophysical
data analysis. Selection criteria for observed astronomical objects are often
too complicated to be derived from first principles. However the existence of
an observed group of objects is a well-suited problem for machine learning
classification.... | computer science |
11,123 | Global Convergence of Online Limited Memory BFGS | math.OC | Global convergence of an online (stochastic) limited memory version of the
Broyden-Fletcher- Goldfarb-Shanno (BFGS) quasi-Newton method for solving
optimization problems with stochastic objectives that arise in large scale
machine learning is established. Lower and upper bounds on the Hessian
eigenvalues of the sample ... | computer science |
11,124 | A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate | cs.LG | We describe and analyze a simple algorithm for principal component analysis
and singular value decomposition, VR-PCA, which uses computationally cheap
stochastic iterations, yet converges exponentially fast to the optimal
solution. In contrast, existing algorithms suffer either from slow convergence,
or computationally... | computer science |
11,125 | Topic Modeling of Hierarchical Corpora | stat.ML | We study the problem of topic modeling in corpora whose documents are
organized in a multi-level hierarchy. We explore a parametric approach to this
problem, assuming that the number of topics is known or can be estimated by
cross-validation. The models we consider can be viewed as special
(finite-dimensional) instance... | computer science |
11,126 | Optimization Methods for Sparse Pseudo-Likelihood Graphical Model
Selection | stat.CO | Sparse high dimensional graphical model selection is a popular topic in
contemporary machine learning. To this end, various useful approaches have been
proposed in the context of $\ell_1$-penalized estimation in the Gaussian
framework. Though many of these inverse covariance estimation approaches are
demonstrably scala... | computer science |
11,127 | Tight Error Bounds for Structured Prediction | cs.LG | Structured prediction tasks in machine learning involve the simultaneous
prediction of multiple labels. This is typically done by maximizing a score
function on the space of labels, which decomposes as a sum of pairwise
elements, each depending on two specific labels. Intuitively, the more pairwise
terms are used, the ... | computer science |
11,128 | Improving Cross-domain Recommendation through Probabilistic
Cluster-level Latent Factor Model--Extended Version | cs.IR | Cross-domain recommendation has been proposed to transfer user behavior
pattern by pooling together the rating data from multiple domains to alleviate
the sparsity problem appearing in single rating domains. However, previous
models only assume that multiple domains share a latent common rating pattern
based on the use... | computer science |
11,129 | Unsupervised learning of regression mixture models with unknown number
of components | stat.ME | Regression mixture models are widely studied in statistics, machine learning
and data analysis. Fitting regression mixtures is challenging and is usually
performed by maximum likelihood by using the expectation-maximization (EM)
algorithm. However, it is well-known that the initialization is crucial for EM.
If the init... | computer science |
11,130 | Variational Pseudolikelihood for Regularized Ising Inference | cs.LG | I propose a variational approach to maximum pseudolikelihood inference of the
Ising model. The variational algorithm is more computationally efficient, and
does a better job predicting out-of-sample correlations than $L_2$ regularized
maximum pseudolikelihood inference as well as mean field and isolated spin pair
appro... | computer science |
11,131 | Adaptive Low-Complexity Sequential Inference for Dirichlet Process
Mixture Models | stat.ML | We develop a sequential low-complexity inference procedure for Dirichlet
process mixtures of Gaussians for online clustering and parameter estimation
when the number of clusters are unknown a-priori. We present an easily
computable, closed form parametric expression for the conditional likelihood,
in which hyperparamet... | computer science |
11,132 | A Bayesian Tensor Factorization Model via Variational Inference for Link
Prediction | cs.LG | Probabilistic approaches for tensor factorization aim to extract meaningful
structure from incomplete data by postulating low rank constraints. Recently,
variational Bayesian (VB) inference techniques have successfully been applied
to large scale models. This paper presents full Bayesian inference via VB on
both single... | computer science |
11,133 | Bayesian and regularization approaches to multivariable linear system
identification: the role of rank penalties | cs.SY | Recent developments in linear system identification have proposed the use of
non-parameteric methods, relying on regularization strategies, to handle the
so-called bias/variance trade-off. This paper introduces an impulse response
estimator which relies on an $\ell_2$-type regularization including a
rank-penalty derive... | computer science |
11,134 | Distributed Detection : Finite-time Analysis and Impact of Network
Topology | math.OC | This paper addresses the problem of distributed detection in multi-agent
networks. Agents receive private signals about an unknown state of the world.
The underlying state is globally identifiable, yet informative signals may be
dispersed throughout the network. Using an optimization-based framework, we
develop an iter... | computer science |
11,135 | Generalized Low Rank Models | stat.ML | Principal components analysis (PCA) is a well-known technique for
approximating a tabular data set by a low rank matrix. Here, we extend the idea
of PCA to handle arbitrary data sets consisting of numerical, Boolean,
categorical, ordinal, and other data types. This framework encompasses many
well known techniques in da... | computer science |
11,136 | Deterministic Conditions for Subspace Identifiability from Incomplete
Sampling | stat.ML | Consider a generic $r$-dimensional subspace of $\mathbb{R}^d$, $r<d$, and
suppose that we are only given projections of this subspace onto small subsets
of the canonical coordinates. The paper establishes necessary and sufficient
deterministic conditions on the subsets for subspace identifiability. | computer science |
11,137 | Probit Normal Correlated Topic Models | stat.ML | The logistic normal distribution has recently been adapted via the
transformation of multivariate Gaus- sian variables to model the topical
distribution of documents in the presence of correlations among topics. In this
paper, we propose a probit normal alternative approach to modelling correlated
topical structures. O... | computer science |
11,138 | Minimax Analysis of Active Learning | cs.LG | This work establishes distribution-free upper and lower bounds on the minimax
label complexity of active learning with general hypothesis classes, under
various noise models. The results reveal a number of surprising facts. In
particular, under the noise model of Tsybakov (2004), the minimax label
complexity of active ... | computer science |
11,139 | Speculate-Correct Error Bounds for k-Nearest Neighbor Classifiers | cs.LG | We introduce the speculate-correct method to derive error bounds for local
classifiers. Using it, we show that k nearest neighbor classifiers, in spite of
their famously fractured decision boundaries, have exponential error bounds
with O(sqrt((k + ln n) / n)) error bound range for n in-sample examples. | computer science |
11,140 | Multi-Scale Local Shape Analysis and Feature Selection in Machine
Learning Applications | cs.CG | We introduce a method called multi-scale local shape analysis, or MLSA, for
extracting features that describe the local structure of points within a
dataset. The method uses both geometric and topological features at multiple
levels of granularity to capture diverse types of local information for
subsequent machine lea... | computer science |
11,141 | Ricci Curvature and the Manifold Learning Problem | math.DG | Consider a sample of $n$ points taken i.i.d from a submanifold $\Sigma$ of
Euclidean space. We show that there is a way to estimate the Ricci curvature of
$\Sigma$ with respect to the induced metric from the sample. Our method is
grounded in the notions of Carr\'e du Champ for diffusion semi-groups, the
theory of Empir... | computer science |
11,142 | Detection of cheating by decimation algorithm | stat.ML | We expand the item response theory to study the case of "cheating students"
for a set of exams, trying to detect them by applying a greedy algorithm of
inference. This extended model is closely related to the Boltzmann machine
learning. In this paper we aim to infer the correct biases and interactions of
our model by c... | computer science |
11,143 | Tighter Low-rank Approximation via Sampling the Leveraged Element | cs.DS | In this work, we propose a new randomized algorithm for computing a low-rank
approximation to a given matrix. Taking an approach different from existing
literature, our method first involves a specific biased sampling, with an
element being chosen based on the leverage scores of its row and column, and
then involves we... | computer science |
11,144 | Thompson sampling with the online bootstrap | cs.LG | Thompson sampling provides a solution to bandit problems in which new
observations are allocated to arms with the posterior probability that an arm
is optimal. While sometimes easy to implement and asymptotically optimal,
Thompson sampling can be computationally demanding in large scale bandit
problems, and its perform... | computer science |
11,145 | Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms
for Machine Learning | stat.ML | Frank-Wolfe algorithms for convex minimization have recently gained
considerable attention from the Optimization and Machine Learning communities,
as their properties make them a suitable choice in a variety of applications.
However, as each iteration requires to optimize a linear model, a clever
implementation is cruc... | computer science |
11,146 | Multi-Level Anomaly Detection on Time-Varying Graph Data | cs.SI | This work presents a novel modeling and analysis framework for graph
sequences which addresses the challenge of detecting and contextualizing
anomalies in labelled, streaming graph data. We introduce a generalization of
the BTER model of Seshadhri et al. by adding flexibility to community
structure, and use this model ... | computer science |
11,147 | Generalized Conditional Gradient for Sparse Estimation | math.OC | Structured sparsity is an important modeling tool that expands the
applicability of convex formulations for data analysis, however it also creates
significant challenges for efficient algorithm design. In this paper we
investigate the generalized conditional gradient (GCG) algorithm for solving
structured sparse optimi... | computer science |
11,148 | Gaussian Process Models with Parallelization and GPU acceleration | cs.DC | In this work, we present an extension of Gaussian process (GP) models with
sophisticated parallelization and GPU acceleration. The parallelization scheme
arises naturally from the modular computational structure w.r.t. datapoints in
the sparse Gaussian process formulation. Additionally, the computational
bottleneck is ... | computer science |
11,149 | Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner
Product Search (MIPS) | stat.ML | Recently it was shown that the problem of Maximum Inner Product Search (MIPS)
is efficient and it admits provably sub-linear hashing algorithms. Asymmetric
transformations before hashing were the key in solving MIPS which was otherwise
hard. In the prior work, the authors use asymmetric transformations which
convert th... | computer science |
11,150 | On Symmetric and Asymmetric LSHs for Inner Product Search | stat.ML | We consider the problem of designing locality sensitive hashes (LSH) for
inner product similarity, and of the power of asymmetric hashes in this
context. Shrivastava and Li argue that there is no symmetric LSH for the
problem and propose an asymmetric LSH based on different mappings for query and
database points. Howev... | computer science |
11,151 | Online Energy Price Matrix Factorization for Power Grid Topology
Tracking | stat.ML | Grid security and open markets are two major smart grid goals. Transparency
of market data facilitates a competitive and efficient energy environment, yet
it may also reveal critical physical system information. Recovering the grid
topology based solely on publicly available market data is explored here.
Real-time ener... | computer science |
11,152 | Model Selection for Topic Models via Spectral Decomposition | stat.ML | Topic models have achieved significant successes in analyzing large-scale
text corpus. In practical applications, we are always confronted with the
challenge of model selection, i.e., how to appropriately set the number of
topics. Following recent advances in topic model inference via tensor
decomposition, we make a fi... | computer science |
11,153 | Covariance Matrices for Mean Field Variational Bayes | stat.ML | Mean Field Variational Bayes (MFVB) is a popular posterior approximation
method due to its fast runtime on large-scale data sets. However, it is well
known that a major failing of MFVB is its (sometimes severe) underestimates of
the uncertainty of model variables and lack of information about model variable
covariance.... | computer science |
11,154 | Sparse Distributed Learning via Heterogeneous Diffusion Adaptive
Networks | cs.LG | In-network distributed estimation of sparse parameter vectors via diffusion
LMS strategies has been studied and investigated in recent years. In all the
existing works, some convex regularization approach has been used at each node
of the network in order to achieve an overall network performance superior to
that of th... | computer science |
11,155 | Heteroscedastic Treed Bayesian Optimisation | cs.LG | Optimising black-box functions is important in many disciplines, such as
tuning machine learning models, robotics, finance and mining exploration.
Bayesian optimisation is a state-of-the-art technique for the global
optimisation of black-box functions which are expensive to evaluate. At the
core of this approach is a G... | computer science |
11,156 | Maximally Informative Hierarchical Representations of High-Dimensional
Data | stat.ML | We consider a set of probabilistic functions of some input variables as a
representation of the inputs. We present bounds on how informative a
representation is about input data. We extend these bounds to hierarchical
representations so that we can quantify the contribution of each layer towards
capturing the informati... | computer science |
11,157 | Non-convex Robust PCA | cs.IT | We propose a new method for robust PCA -- the task of recovering a low-rank
matrix from sparse corruptions that are of unknown value and support. Our
method involves alternating between projecting appropriate residuals onto the
set of low-rank matrices, and the set of sparse matrices; each projection is
{\em non-convex... | computer science |
11,158 | Latent Feature Based FM Model For Rating Prediction | cs.LG | Rating Prediction is a basic problem in Recommender System, and one of the
most widely used method is Factorization Machines(FM). However, traditional
matrix factorization methods fail to utilize the benefit of implicit feedback,
which has been proved to be important in Rating Prediction problem. In this
work, we consi... | computer science |
11,159 | Bootstrap-Based Regularization for Low-Rank Matrix Estimation | stat.ME | We develop a flexible framework for low-rank matrix estimation that allows us
to transform noise models into regularization schemes via a simple bootstrap
algorithm. Effectively, our procedure seeks an autoencoding basis for the
observed matrix that is stable with respect to the specified noise model; we
call the resul... | computer science |
11,160 | Greedy Subspace Clustering | stat.ML | We consider the problem of subspace clustering: given points that lie on or
near the union of many low-dimensional linear subspaces, recover the subspaces.
To this end, one first identifies sets of points close to the same subspace and
uses the sets to estimate the subspaces. As the geometric structure of the
clusters ... | computer science |
11,161 | Robust sketching for multiple square-root LASSO problems | math.OC | Many learning tasks, such as cross-validation, parameter search, or
leave-one-out analysis, involve multiple instances of similar problems, each
instance sharing a large part of learning data with the others. We introduce a
robust framework for solving multiple square-root LASSO problems, based on a
sketch of the learn... | computer science |
11,162 | Fast Randomized Kernel Methods With Statistical Guarantees | stat.ML | One approach to improving the running time of kernel-based machine learning
methods is to build a small sketch of the input and use it in lieu of the full
kernel matrix in the machine learning task of interest. Here, we describe a
version of this approach that comes with running time guarantees as well as
improved guar... | computer science |
11,163 | Bayesian feature selection with strongly-regularizing priors maps to the
Ising Model | cs.LG | Identifying small subsets of features that are relevant for prediction and/or
classification tasks is a central problem in machine learning and statistics.
The feature selection task is especially important, and computationally
difficult, for modern datasets where the number of features can be comparable
to, or even ex... | computer science |
11,164 | Convex Optimization for Big Data | math.OC | This article reviews recent advances in convex optimization algorithms for
Big Data, which aim to reduce the computational, storage, and communications
bottlenecks. We provide an overview of this emerging field, describe
contemporary approximation techniques like first-order methods and
randomization for scalability, a... | computer science |
11,165 | A statistical model for tensor PCA | cs.LG | We consider the Principal Component Analysis problem for large tensors of
arbitrary order $k$ under a single-spike (or rank-one plus noise) model. On the
one hand, we use information theory, and recent results in probability theory,
to establish necessary and sufficient conditions under which the principal
component ca... | computer science |
11,166 | Global Convergence of Stochastic Gradient Descent for Some Non-convex
Matrix Problems | cs.LG | Stochastic gradient descent (SGD) on a low-rank factorization is commonly
employed to speed up matrix problems including matrix completion, subspace
tracking, and SDP relaxation. In this paper, we exhibit a step size scheme for
SGD on a low-rank least-squares problem, and we prove that, under broad
sampling conditions,... | computer science |
11,167 | Model-Parallel Inference for Big Topic Models | cs.DC | In real world industrial applications of topic modeling, the ability to
capture gigantic conceptual space by learning an ultra-high dimensional topical
representation, i.e., the so-called "big model", is becoming the next
desideratum after enthusiasms on "big data", especially for fine-grained
downstream tasks such as ... | computer science |
11,168 | On TD(0) with function approximation: Concentration bounds and a
centered variant with exponential convergence | cs.LG | We provide non-asymptotic bounds for the well-known temporal difference
learning algorithm TD(0) with linear function approximators. These include
high-probability bounds as well as bounds in expectation. Our analysis suggests
that a step-size inversely proportional to the number of iterations cannot
guarantee optimal ... | computer science |
11,169 | Deep Narrow Boltzmann Machines are Universal Approximators | stat.ML | We show that deep narrow Boltzmann machines are universal approximators of
probability distributions on the activities of their visible units, provided
they have sufficiently many hidden layers, each containing the same number of
units as the visible layer. We show that, within certain parameter domains,
deep Boltzmann... | computer science |
11,170 | A unified view of generative models for networks: models, methods,
opportunities, and challenges | stat.ML | Research on probabilistic models of networks now spans a wide variety of
fields, including physics, sociology, biology, statistics, and machine
learning. These efforts have produced a diverse ecology of models and methods.
Despite this diversity, many of these models share a common underlying
structure: pairwise intera... | computer science |
11,171 | Parallel Gaussian Process Regression for Big Data: Low-Rank
Representation Meets Markov Approximation | stat.ML | The expressive power of a Gaussian process (GP) model comes at a cost of poor
scalability in the data size. To improve its scalability, this paper presents a
low-rank-cum-Markov approximation (LMA) of the GP model that is novel in
leveraging the dual computational advantages stemming from complementing a
low-rank appro... | computer science |
11,172 | Unification of field theory and maximum entropy methods for learning
probability densities | cs.LG | The need to estimate smooth probability distributions (a.k.a. probability
densities) from finite sampled data is ubiquitous in science. Many approaches
to this problem have been described, but none is yet regarded as providing a
definitive solution. Maximum entropy estimation and Bayesian field theory are
two such appr... | computer science |
11,173 | Private Empirical Risk Minimization Beyond the Worst Case: The Effect of
the Constraint Set Geometry | cs.LG | Empirical Risk Minimization (ERM) is a standard technique in machine
learning, where a model is selected by minimizing a loss function over
constraint set. When the training dataset consists of private information, it
is natural to use a differentially private ERM algorithm, and this problem has
been the subject of a l... | computer science |
11,174 | On the Impossibility of Convex Inference in Human Computation | stat.ML | Human computation or crowdsourcing involves joint inference of the
ground-truth-answers and the worker-abilities by optimizing an objective
function, for instance, by maximizing the data likelihood based on an assumed
underlying model. A variety of methods have been proposed in the literature to
address this inference ... | computer science |
11,175 | Clustering evolving data using kernel-based methods | cs.SI | In this thesis, we propose several modelling strategies to tackle evolving
data in different contexts. In the framework of static clustering, we start by
introducing a soft kernel spectral clustering (SKSC) algorithm, which can
better deal with overlapping clusters with respect to kernel spectral
clustering (KSC) and p... | computer science |
11,176 | PU Learning for Matrix Completion | cs.LG | In this paper, we consider the matrix completion problem when the
observations are one-bit measurements of some underlying matrix M, and in
particular the observed samples consist only of ones and no zeros. This problem
is motivated by modern applications such as recommender systems and social
networks where only "like... | computer science |
11,177 | Efficiently learning Ising models on arbitrary graphs | cs.LG | We consider the problem of reconstructing the graph underlying an Ising model
from i.i.d. samples. Over the last fifteen years this problem has been of
significant interest in the statistics, machine learning, and statistical
physics communities, and much of the effort has been directed towards finding
algorithms with ... | computer science |
11,178 | Target Fishing: A Single-Label or Multi-Label Problem? | cs.LG | According to Cobanoglu et al and Murphy, it is now widely acknowledged that
the single target paradigm (one protein or target, one disease, one drug) that
has been the dominant premise in drug development in the recent past is
untenable. More often than not, a drug-like compound (ligand) can be
promiscuous - that is, i... | computer science |
11,179 | Consistency of Cheeger and Ratio Graph Cuts | stat.ML | This paper establishes the consistency of a family of graph-cut-based
algorithms for clustering of data clouds. We consider point clouds obtained as
samples of a ground-truth measure. We investigate approaches to clustering
based on minimizing objective functionals defined on proximity graphs of the
given sample. Our f... | computer science |
11,180 | A Latent Source Model for Online Collaborative Filtering | cs.LG | Despite the prevalence of collaborative filtering in recommendation systems,
there has been little theoretical development on why and how well it works,
especially in the "online" setting, where items are recommended to users over
time. We address this theoretical gap by introducing a model for online
recommendation sy... | computer science |
11,181 | Noise Benefits in Expectation-Maximization Algorithms | stat.ML | This dissertation shows that careful injection of noise into sample data can
substantially speed up Expectation-Maximization algorithms.
Expectation-Maximization algorithms are a class of iterative algorithms for
extracting maximum likelihood estimates from corrupted or incomplete data. The
convergence speed-up is an e... | computer science |
11,182 | Heuristics for Exact Nonnegative Matrix Factorization | math.OC | The exact nonnegative matrix factorization (exact NMF) problem is the
following: given an $m$-by-$n$ nonnegative matrix $X$ and a factorization rank
$r$, find, if possible, an $m$-by-$r$ nonnegative matrix $W$ and an $r$-by-$n$
nonnegative matrix $H$ such that $X = WH$. In this paper, we propose two
heuristics for exac... | computer science |
11,183 | Signal Recovery on Graphs: Variation Minimization | cs.SI | We consider the problem of signal recovery on graphs as graphs model data
with complex structure as signals on a graph. Graph signal recovery implies
recovery of one or multiple smooth graph signals from noisy, corrupted, or
incomplete measurements. We propose a graph signal model and formulate signal
recovery as a cor... | computer science |
11,184 | Learning with Algebraic Invariances, and the Invariant Kernel Trick | stat.ML | When solving data analysis problems it is important to integrate prior
knowledge and/or structural invariances. This paper contributes by a novel
framework for incorporating algebraic invariance structure into kernels. In
particular, we show that algebraic properties such as sign symmetries in data,
phase independence,... | computer science |
11,185 | Predicting clicks in online display advertising with latent features and
side-information | stat.ML | We review a method for click-through rate prediction based on the work of
Menon et al. [11], which combines collaborative filtering and matrix
factorization with a side-information model and fuses the outputs to proper
probabilities in [0,1]. In addition we provide details, both for the modeling
as well as the experime... | computer science |
11,186 | Constant Step Size Least-Mean-Square: Bias-Variance Trade-offs and
Optimal Sampling Distributions | cs.LG | We consider the least-squares regression problem and provide a detailed
asymptotic analysis of the performance of averaged constant-step-size
stochastic gradient descent (a.k.a. least-mean-squares). In the strongly-convex
case, we provide an asymptotic expansion up to explicit exponentially decaying
terms. Our analysis... | computer science |
11,187 | Learning interpretable models of phenotypes from whole genome sequences
with the Set Covering Machine | cs.CE | The increased affordability of whole genome sequencing has motivated its use
for phenotypic studies. We address the problem of learning interpretable models
for discrete phenotypes from whole genomes. We propose a general approach that
relies on the Set Covering Machine and a k-mer representation of the genomes.
We sho... | computer science |
11,188 | Structure learning of antiferromagnetic Ising models | stat.ML | In this paper we investigate the computational complexity of learning the
graph structure underlying a discrete undirected graphical model from i.i.d.
samples. We first observe that the notoriously difficult problem of learning
parities with noise can be captured as a special case of learning graphical
models. This lea... | computer science |
11,189 | LightLDA: Big Topic Models on Modest Compute Clusters | stat.ML | When building large-scale machine learning (ML) programs, such as big topic
models or deep neural nets, one usually assumes such tasks can only be
attempted with industrial-sized clusters with thousands of nodes, which are out
of reach for most practitioners or academic researchers. We consider this
challenge in the co... | computer science |
11,190 | MLitB: Machine Learning in the Browser | cs.DC | With few exceptions, the field of Machine Learning (ML) research has largely
ignored the browser as a computational engine. Beyond an educational resource
for ML, the browser has vast potential to not only improve the state-of-the-art
in ML research, but also, inexpensively and on a massive scale, to bring
sophisticate... | computer science |
11,191 | Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample
Complexity | cs.DS | We present a simple, general technique for reducing the sample complexity of
matrix and tensor decomposition algorithms applied to distributions. We use the
technique to give a polynomial-time algorithm for standard ICA with sample
complexity nearly linear in the dimension, thereby improving substantially on
previous b... | computer science |
11,192 | Sparsity and adaptivity for the blind separation of partially correlated
sources | stat.AP | Blind source separation (BSS) is a very popular technique to analyze
multichannel data. In this context, the data are modeled as the linear
combination of sources to be retrieved. For that purpose, standard BSS methods
all rely on some discrimination principle, whether it is statistical
independence or morphological di... | computer science |
11,193 | The Statistics of Streaming Sparse Regression | math.ST | We present a sparse analogue to stochastic gradient descent that is
guaranteed to perform well under similar conditions to the lasso. In the linear
regression setup with irrepresentable noise features, our algorithm recovers
the support set of the optimal parameter vector with high probability, and
achieves a statistic... | computer science |
11,194 | Testing MCMC code | cs.SE | Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic
modeling and inference, but are difficult to debug, and are prone to silent
failure if implemented naively. We outline several strategies for testing the
correctness of MCMC algorithms. Specifically, we advocate writing code in a
modular way, w... | computer science |
11,195 | Theoretical and Numerical Analysis of Approximate Dynamic Programming
with Approximation Errors | cs.SY | This study is aimed at answering the famous question of how the approximation
errors at each iteration of Approximate Dynamic Programming (ADP) affect the
quality of the final results considering the fact that errors at each iteration
affect the next iteration. To this goal, convergence of Value Iteration scheme
of ADP... | computer science |
11,196 | Pathwise Coordinate Optimization for Sparse Learning: Algorithm and
Theory | stat.ML | The pathwise coordinate optimization is one of the most important
computational frameworks for high dimensional convex and nonconvex sparse
learning problems. It differs from the classical coordinate optimization
algorithms in three salient features: {\it warm start initialization}, {\it
active set updating}, and {\it ... | computer science |
11,197 | Cloud K-SVD: A Collaborative Dictionary Learning Algorithm for Big,
Distributed Data | cs.LG | This paper studies the problem of data-adaptive representations for big,
distributed data. It is assumed that a number of geographically-distributed,
interconnected sites have massive local data and they are interested in
collaboratively learning a low-dimensional geometric structure underlying these
data. In contrast ... | computer science |
11,198 | Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels | stat.ML | We consider the problem of improving the efficiency of randomized Fourier
feature maps to accelerate training and testing speed of kernel methods on
large datasets. These approximate feature maps arise as Monte Carlo
approximations to integral representations of shift-invariant kernel functions
(e.g., Gaussian kernel).... | computer science |
11,199 | An ADMM algorithm for solving a proximal bound-constrained quadratic
program | math.OC | We consider a proximal operator given by a quadratic function subject to
bound constraints and give an optimization algorithm using the alternating
direction method of multipliers (ADMM). The algorithm is particularly efficient
to solve a collection of proximal operators that share the same quadratic form,
or if the qu... | computer science |
11,200 | Communication-Efficient Distributed Optimization of Self-Concordant
Empirical Loss | math.OC | We consider distributed convex optimization problems originated from sample
average approximation of stochastic optimization, or empirical risk
minimization in machine learning. We assume that each machine in the
distributed computing system has access to a local empirical loss function,
constructed with i.i.d. data sa... | computer science |
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