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10,701 | Learning Module Networks | cs.LG | Methods for learning Bayesian network structure can discover dependency
structure between observed variables, and have been shown to be useful in many
applications. However, in domains that involve a large number of variables, the
space of possible network structures is enormous, making it difficult, for both
computati... | computer science |
10,702 | Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs | cs.LG | We consider the problem of learning the structure of undirected graphical
models with bounded treewidth, within the maximum likelihood framework. This is
an NP-hard problem and most approaches consider local search techniques. In
this paper, we pose it as a combinatorial optimization problem, which is then
relaxed to a... | computer science |
10,703 | Bayesian one-mode projection for dynamic bipartite graphs | stat.ML | We propose a Bayesian methodology for one-mode projecting a bipartite network
that is being observed across a series of discrete time steps. The resulting
one mode network captures the uncertainty over the presence/absence of each
link and provides a probability distribution over its possible weight values.
Additionall... | computer science |
10,704 | Dictionary Subselection Using an Overcomplete Joint Sparsity Model | cs.LG | Many natural signals exhibit a sparse representation, whenever a suitable
describing model is given. Here, a linear generative model is considered, where
many sparsity-based signal processing techniques rely on such a simplified
model. As this model is often unknown for many classes of the signals, we need
to select su... | computer science |
10,705 | Belief Propagation for Continuous State Spaces: Stochastic
Message-Passing with Quantitative Guarantees | cs.IT | The sum-product or belief propagation (BP) algorithm is a widely used
message-passing technique for computing approximate marginals in graphical
models. We introduce a new technique, called stochastic orthogonal series
message-passing (SOSMP), for computing the BP fixed point in models with
continuous random variables.... | computer science |
10,706 | Alternating Maximization: Unifying Framework for 8 Sparse PCA
Formulations and Efficient Parallel Codes | stat.ML | Given a multivariate data set, sparse principal component analysis (SPCA)
aims to extract several linear combinations of the variables that together
explain the variance in the data as much as possible, while controlling the
number of nonzero loadings in these combinations. In this paper we consider 8
different optimiz... | computer science |
10,707 | Feature Clustering for Accelerating Parallel Coordinate Descent | stat.ML | Large-scale L1-regularized loss minimization problems arise in
high-dimensional applications such as compressed sensing and high-dimensional
supervised learning, including classification and regression problems.
High-performance algorithms and implementations are critical to efficiently
solving these problems. Building... | computer science |
10,708 | Variational Optimization | stat.ML | We discuss a general technique that can be used to form a differentiable
bound on the optima of non-differentiable or discrete objective functions. We
form a unified description of these methods and consider under which
circumstances the bound is concave. In particular we consider two concrete
applications of the metho... | computer science |
10,709 | Role Mining with Probabilistic Models | cs.CR | Role mining tackles the problem of finding a role-based access control (RBAC)
configuration, given an access-control matrix assigning users to access
permissions as input. Most role mining approaches work by constructing a large
set of candidate roles and use a greedy selection strategy to iteratively pick
a small subs... | computer science |
10,710 | A Practical Algorithm for Topic Modeling with Provable Guarantees | cs.LG | Topic models provide a useful method for dimensionality reduction and
exploratory data analysis in large text corpora. Most approaches to topic model
inference have been based on a maximum likelihood objective. Efficient
algorithms exist that approximate this objective, but they have no provable
guarantees. Recently, a... | computer science |
10,711 | Nonparametric ridge estimation | math.ST | We study the problem of estimating the ridges of a density function. Ridge
estimation is an extension of mode finding and is useful for understanding the
structure of a density. It can also be used to find hidden structure in point
cloud data. We show that, under mild regularity conditions, the ridges of the
kernel den... | computer science |
10,712 | Exponentially Weighted Moving Average Charts for Detecting Concept Drift | stat.ML | Classifying streaming data requires the development of methods which are
computationally efficient and able to cope with changes in the underlying
distribution of the stream, a phenomenon known in the literature as concept
drift. We propose a new method for detecting concept drift which uses an
Exponentially Weighted M... | computer science |
10,713 | Hyperplane Arrangements and Locality-Sensitive Hashing with Lift | cs.LG | Locality-sensitive hashing converts high-dimensional feature vectors, such as
image and speech, into bit arrays and allows high-speed similarity calculation
with the Hamming distance. There is a hashing scheme that maps feature vectors
to bit arrays depending on the signs of the inner products between feature
vectors a... | computer science |
10,714 | Distribution-Free Distribution Regression | stat.ML | `Distribution regression' refers to the situation where a response Y depends
on a covariate P where P is a probability distribution. The model is Y=f(P) +
mu where f is an unknown regression function and mu is a random error.
Typically, we do not observe P directly, but rather, we observe a sample from
P. In this paper... | computer science |
10,715 | Bounded regret in stochastic multi-armed bandits | math.ST | We study the stochastic multi-armed bandit problem when one knows the value
$\mu^{(\star)}$ of an optimal arm, as a well as a positive lower bound on the
smallest positive gap $\Delta$. We propose a new randomized policy that attains
a regret {\em uniformly bounded over time} in this setting. We also prove
several lowe... | computer science |
10,716 | Feature Selection for Microarray Gene Expression Data using Simulated
Annealing guided by the Multivariate Joint Entropy | cs.CE | In this work a new way to calculate the multivariate joint entropy is
presented. This measure is the basis for a fast information-theoretic based
evaluation of gene relevance in a Microarray Gene Expression data context. Its
low complexity is based on the reuse of previous computations to calculate
current feature rele... | computer science |
10,717 | Latent Self-Exciting Point Process Model for Spatial-Temporal Networks | cs.SI | We propose a latent self-exciting point process model that describes
geographically distributed interactions between pairs of entities. In contrast
to most existing approaches that assume fully observable interactions, here we
consider a scenario where certain interaction events lack information about
participants. Ins... | computer science |
10,718 | Competing With Strategies | stat.ML | We study the problem of online learning with a notion of regret defined with
respect to a set of strategies. We develop tools for analyzing the minimax
rates and for deriving regret-minimization algorithms in this scenario. While
the standard methods for minimizing the usual notion of regret fail, through
our analysis ... | computer science |
10,719 | A Tensor Approach to Learning Mixed Membership Community Models | cs.LG | Community detection is the task of detecting hidden communities from observed
interactions. Guaranteed community detection has so far been mostly limited to
models with non-overlapping communities such as the stochastic block model. In
this paper, we remove this restriction, and provide guaranteed community
detection f... | computer science |
10,720 | Adaptive Metric Dimensionality Reduction | cs.LG | We study adaptive data-dependent dimensionality reduction in the context of
supervised learning in general metric spaces. Our main statistical contribution
is a generalization bound for Lipschitz functions in metric spaces that are
doubling, or nearly doubling. On the algorithmic front, we describe an analogue
of PCA f... | computer science |
10,721 | Bayesian Learning of Loglinear Models for Neural Connectivity | cs.LG | This paper presents a Bayesian approach to learning the connectivity
structure of a group of neurons from data on configuration frequencies. A major
objective of the research is to provide statistical tools for detecting changes
in firing patterns with changing stimuli. Our framework is not restricted to
the well-under... | computer science |
10,722 | A Latent Source Model for Nonparametric Time Series Classification | stat.ML | For classifying time series, a nearest-neighbor approach is widely used in
practice with performance often competitive with or better than more elaborate
methods such as neural networks, decision trees, and support vector machines.
We develop theoretical justification for the effectiveness of
nearest-neighbor-like clas... | computer science |
10,723 | Bio-inspired data mining: Treating malware signatures as biosequences | cs.LG | The application of machine learning to bioinformatics problems is well
established. Less well understood is the application of bioinformatics
techniques to machine learning and, in particular, the representation of
non-biological data as biosequences. The aim of this paper is to explore the
effects of giving amino acid... | computer science |
10,724 | On Translation Invariant Kernels and Screw Functions | math.FA | We explore the connection between Hilbertian metrics and positive definite
kernels on the real line. In particular, we look at a well-known
characterization of translation invariant Hilbertian metrics on the real line
by von Neumann and Schoenberg (1941). Using this result we are able to give an
alternate proof of Boch... | computer science |
10,725 | Optimal Discriminant Functions Based On Sampled Distribution Distance
for Modulation Classification | stat.ML | In this letter, we derive the optimal discriminant functions for modulation
classification based on the sampled distribution distance. The proposed method
classifies various candidate constellations using a low complexity approach
based on the distribution distance at specific testpoints along the cumulative
distributi... | computer science |
10,726 | Structure Discovery in Nonparametric Regression through Compositional
Kernel Search | stat.ML | Despite its importance, choosing the structural form of the kernel in
nonparametric regression remains a black art. We define a space of kernel
structures which are built compositionally by adding and multiplying a small
number of base kernels. We present a method for searching over this space of
structures which mirro... | computer science |
10,727 | On learning parametric-output HMMs | cs.LG | We present a novel approach for learning an HMM whose outputs are distributed
according to a parametric family. This is done by {\em decoupling} the learning
task into two steps: first estimating the output parameters, and then
estimating the hidden states transition probabilities. The first step is
accomplished by fit... | computer science |
10,728 | Phoneme discrimination using $KS$-algebra II | cs.SD | $KS$-algebra consists of expressions constructed with four kinds operations,
the minimum, maximum, difference and additively homogeneous generalized means.
Five families of $Z$-classifiers are investigated on binary classification
tasks between English phonemes. It is shown that the classifiers are able to
reflect well... | computer science |
10,729 | Missing Entries Matrix Approximation and Completion | math.NA | We describe several algorithms for matrix completion and matrix approximation
when only some of its entries are known. The approximation constraint can be
any whose approximated solution is known for the full matrix. For low rank
approximations, similar algorithms appears recently in the literature under
different name... | computer science |
10,730 | Revealing social networks of spammers through spectral clustering | cs.SI | To date, most studies on spam have focused only on the spamming phase of the
spam cycle and have ignored the harvesting phase, which consists of the mass
acquisition of email addresses. It has been observed that spammers conceal
their identity to a lesser degree in the harvesting phase, so it may be
possible to gain ne... | computer science |
10,731 | Tensor Decompositions: A New Concept in Brain Data Analysis? | cs.NA | Matrix factorizations and their extensions to tensor factorizations and
decompositions have become prominent techniques for linear and multilinear
blind source separation (BSS), especially multiway Independent Component
Analysis (ICA), NonnegativeMatrix and Tensor Factorization (NMF/NTF), Smooth
Component Analysis (Smo... | computer science |
10,732 | Feature Selection Based on Term Frequency and T-Test for Text
Categorization | cs.LG | Much work has been done on feature selection. Existing methods are based on
document frequency, such as Chi-Square Statistic, Information Gain etc.
However, these methods have two shortcomings: one is that they are not reliable
for low-frequency terms, and the other is that they only count whether one term
occurs in a ... | computer science |
10,733 | Optimization with First-Order Surrogate Functions | stat.ML | In this paper, we study optimization methods consisting of iteratively
minimizing surrogates of an objective function. By proposing several
algorithmic variants and simple convergence analyses, we make two main
contributions. First, we provide a unified viewpoint for several first-order
optimization techniques such as ... | computer science |
10,734 | Efficient Density Estimation via Piecewise Polynomial Approximation | cs.LG | We give a highly efficient "semi-agnostic" algorithm for learning univariate
probability distributions that are well approximated by piecewise polynomial
density functions. Let $p$ be an arbitrary distribution over an interval $I$
which is $\tau$-close (in total variation distance) to an unknown probability
distributio... | computer science |
10,735 | Online Learning in a Contract Selection Problem | cs.LG | In an online contract selection problem there is a seller which offers a set
of contracts to sequentially arriving buyers whose types are drawn from an
unknown distribution. If there exists a profitable contract for the buyer in
the offered set, i.e., a contract with payoff higher than the payoff of not
accepting any c... | computer science |
10,736 | Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with
Minimax Optimal Rates | math.ST | We establish optimal convergence rates for a decomposition-based scalable
approach to kernel ridge regression. The method is simple to describe: it
randomly partitions a dataset of size N into m subsets of equal size, computes
an independent kernel ridge regression estimator for each subset, then averages
the local sol... | computer science |
10,737 | A Primal Condition for Approachability with Partial Monitoring | math.OC | In approachability with full monitoring there are two types of conditions
that are known to be equivalent for convex sets: a primal and a dual condition.
The primal one is of the form: a set C is approachable if and only all
containing half-spaces are approachable in the one-shot game; while the dual
one is of the form... | computer science |
10,738 | Adapting the Stochastic Block Model to Edge-Weighted Networks | stat.ML | We generalize the stochastic block model to the important case in which edges
are annotated with weights drawn from an exponential family distribution. This
generalization introduces several technical difficulties for model estimation,
which we solve using a Bayesian approach. We introduce a variational algorithm
that ... | computer science |
10,739 | Parallel Gaussian Process Regression with Low-Rank Covariance Matrix
Approximations | stat.ML | Gaussian processes (GP) are Bayesian non-parametric models that are widely
used for probabilistic regression. Unfortunately, it cannot scale well with
large data nor perform real-time predictions due to its cubic time cost in the
data size. This paper presents two parallel GP regression methods that exploit
low-rank co... | computer science |
10,740 | Reinforcement Learning for the Soccer Dribbling Task | cs.LG | We propose a reinforcement learning solution to the \emph{soccer dribbling
task}, a scenario in which a soccer agent has to go from the beginning to the
end of a region keeping possession of the ball, as an adversary attempts to
gain possession. While the adversary uses a stationary policy, the dribbler
learns the best... | computer science |
10,741 | Black Box Variational Inference | stat.ML | Variational inference has become a widely used method to approximate
posteriors in complex latent variables models. However, deriving a variational
inference algorithm generally requires significant model-specific analysis, and
these efforts can hinder and deter us from quickly developing and exploring a
variety of mod... | computer science |
10,742 | Structured Generative Models of Natural Source Code | cs.PL | We study the problem of building generative models of natural source code
(NSC); that is, source code written and understood by humans. Our primary
contribution is to describe a family of generative models for NSC that have
three key properties: First, they incorporate both sequential and hierarchical
structure. Second... | computer science |
10,743 | More Algorithms for Provable Dictionary Learning | cs.DS | In dictionary learning, also known as sparse coding, the algorithm is given
samples of the form $y = Ax$ where $x\in \mathbb{R}^m$ is an unknown random
sparse vector and $A$ is an unknown dictionary matrix in $\mathbb{R}^{n\times
m}$ (usually $m > n$, which is the overcomplete case). The goal is to learn $A$
and $x$. T... | computer science |
10,744 | Concave Penalized Estimation of Sparse Gaussian Bayesian Networks | stat.ME | We develop a penalized likelihood estimation framework to estimate the
structure of Gaussian Bayesian networks from observational data. In contrast to
recent methods which accelerate the learning problem by restricting the search
space, our main contribution is a fast algorithm for score-based structure
learning which ... | computer science |
10,745 | Learning parametric dictionaries for graph signals | cs.LG | In sparse signal representation, the choice of a dictionary often involves a
tradeoff between two desirable properties -- the ability to adapt to specific
signal data and a fast implementation of the dictionary. To sparsely represent
signals residing on weighted graphs, an additional design challenge is to
incorporate ... | computer science |
10,746 | Actor-Critic Algorithms for Learning Nash Equilibria in N-player
General-Sum Games | cs.GT | We consider the problem of finding stationary Nash equilibria (NE) in a
finite discounted general-sum stochastic game. We first generalize a non-linear
optimization problem from Filar and Vrieze [2004] to a $N$-player setting and
break down this problem into simpler sub-problems that ensure there is no
Bellman error fo... | computer science |
10,747 | Extension of Sparse Randomized Kaczmarz Algorithm for Multiple
Measurement Vectors | cs.NA | The Kaczmarz algorithm is popular for iteratively solving an overdetermined
system of linear equations. The traditional Kaczmarz algorithm can approximate
the solution in few sweeps through the equations but a randomized version of
the Kaczmarz algorithm was shown to converge exponentially and independent of
number of ... | computer science |
10,748 | An Online Expectation-Maximisation Algorithm for Nonnegative Matrix
Factorisation Models | cs.LG | In this paper we formulate the nonnegative matrix factorisation (NMF) problem
as a maximum likelihood estimation problem for hidden Markov models and propose
online expectation-maximisation (EM) algorithms to estimate the NMF and the
other unknown static parameters. We also propose a sequential Monte Carlo
approximatio... | computer science |
10,749 | GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation | cs.LG | Scientists often express their understanding of the world through a
computationally demanding simulation program. Analyzing the posterior
distribution of the parameters given observations (the inverse problem) can be
extremely challenging. The Approximate Bayesian Computation (ABC) framework is
the standard statistical... | computer science |
10,750 | A Boosting Approach to Learning Graph Representations | cs.LG | Learning the right graph representation from noisy, multisource data has
garnered significant interest in recent years. A central tenet of this problem
is relational learning. Here the objective is to incorporate the partial
information each data source gives us in a way that captures the true
underlying relationships.... | computer science |
10,751 | Bayesian Conditional Density Filtering | stat.ML | We propose a Conditional Density Filtering (C-DF) algorithm for efficient
online Bayesian inference. C-DF adapts MCMC sampling to the online setting,
sampling from approximations to conditional posterior distributions obtained by
propagating surrogate conditional sufficient statistics (a function of data and
parameter ... | computer science |
10,752 | The Why and How of Nonnegative Matrix Factorization | stat.ML | Nonnegative matrix factorization (NMF) has become a widely used tool for the
analysis of high-dimensional data as it automatically extracts sparse and
meaningful features from a set of nonnegative data vectors. We first illustrate
this property of NMF on three applications, in image processing, text mining
and hyperspe... | computer science |
10,753 | Ensembled Correlation Between Liver Analysis Outputs | stat.ML | Data mining techniques on the biological analysis are spreading for most of
the areas including the health care and medical information. We have applied
the data mining techniques, such as KNN, SVM, MLP or decision trees over a
unique dataset, which is collected from 16,380 analysis results for a year.
Furthermore we h... | computer science |
10,754 | A continuous-time approach to online optimization | math.OC | We consider a family of learning strategies for online optimization problems
that evolve in continuous time and we show that they lead to no regret. From a
more traditional, discrete-time viewpoint, this continuous-time approach allows
us to derive the no-regret properties of a large class of discrete-time
algorithms i... | computer science |
10,755 | A Stochastic Quasi-Newton Method for Large-Scale Optimization | math.OC | The question of how to incorporate curvature information in stochastic
approximation methods is challenging. The direct application of classical
quasi- Newton updating techniques for deterministic optimization leads to noisy
curvature estimates that have harmful effects on the robustness of the
iteration. In this paper... | computer science |
10,756 | Bayesian Properties of Normalized Maximum Likelihood and its Fast
Computation | cs.IT | The normalized maximized likelihood (NML) provides the minimax regret
solution in universal data compression, gambling, and prediction, and it plays
an essential role in the minimum description length (MDL) method of statistical
modeling and estimation. Here we show that the normalized maximum likelihood
has a Bayes-li... | computer science |
10,757 | Bounding Embeddings of VC Classes into Maximum Classes | cs.LG | One of the earliest conjectures in computational learning theory-the Sample
Compression conjecture-asserts that concept classes (equivalently set systems)
admit compression schemes of size linear in their VC dimension. To-date this
statement is known to be true for maximum classes---those that possess maximum
cardinali... | computer science |
10,758 | RES: Regularized Stochastic BFGS Algorithm | cs.LG | RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno
(BFGS) quasi-Newton method is proposed to solve convex optimization problems
with stochastic objectives. The use of stochastic gradient descent algorithms
is widespread, but the number of iterations required to approximate optimal
arguments c... | computer science |
10,759 | Joint Inference of Multiple Label Types in Large Networks | cs.LG | We tackle the problem of inferring node labels in a partially labeled graph
where each node in the graph has multiple label types and each label type has a
large number of possible labels. Our primary example, and the focus of this
paper, is the joint inference of label types such as hometown, current city,
and employe... | computer science |
10,760 | Principled Graph Matching Algorithms for Integrating Multiple Data
Sources | cs.DB | This paper explores combinatorial optimization for problems of max-weight
graph matching on multi-partite graphs, which arise in integrating multiple
data sources. Entity resolution-the data integration problem of performing
noisy joins on structured data-typically proceeds by first hashing each record
into zero or mor... | computer science |
10,761 | A high-reproducibility and high-accuracy method for automated topic
classification | stat.ML | Much of human knowledge sits in large databases of unstructured text.
Leveraging this knowledge requires algorithms that extract and record metadata
on unstructured text documents. Assigning topics to documents will enable
intelligent search, statistical characterization, and meaningful
classification. Latent Dirichlet... | computer science |
10,762 | Online Stochastic Optimization under Correlated Bandit Feedback | stat.ML | In this paper we consider the problem of online stochastic optimization of a
locally smooth function under bandit feedback. We introduce the high-confidence
tree (HCT) algorithm, a novel any-time $\mathcal{X}$-armed bandit algorithm,
and derive regret bounds matching the performance of existing state-of-the-art
in term... | computer science |
10,763 | Thresholding Classifiers to Maximize F1 Score | stat.ML | This paper provides new insight into maximizing F1 scores in the context of
binary classification and also in the context of multilabel classification. The
harmonic mean of precision and recall, F1 score is widely used to measure the
success of a binary classifier when one class is rare. Micro average, macro
average, a... | computer science |
10,764 | Approachability in unknown games: Online learning meets multi-objective
optimization | stat.ML | In the standard setting of approachability there are two players and a target
set. The players play repeatedly a known vector-valued game where the first
player wants to have the average vector-valued payoff converge to the target
set which the other player tries to exclude it from this set. We revisit this
setting in ... | computer science |
10,765 | A Second-order Bound with Excess Losses | stat.ML | We study online aggregation of the predictions of experts, and first show new
second-order regret bounds in the standard setting, which are obtained via a
version of the Prod algorithm (and also a version of the polynomially weighted
average algorithm) with multiple learning rates. These bounds are in terms of
excess l... | computer science |
10,766 | Universal Matrix Completion | stat.ML | The problem of low-rank matrix completion has recently generated a lot of
interest leading to several results that offer exact solutions to the problem.
However, in order to do so, these methods make assumptions that can be quite
restrictive in practice. More specifically, the methods assume that: a) the
observed indic... | computer science |
10,767 | Online Nonparametric Regression | stat.ML | We establish optimal rates for online regression for arbitrary classes of
regression functions in terms of the sequential entropy introduced in (Rakhlin,
Sridharan, Tewari, 2010). The optimal rates are shown to exhibit a phase
transition analogous to the i.i.d./statistical learning case, studied in
(Rakhlin, Sridharan,... | computer science |
10,768 | Ranking via Robust Binary Classification and Parallel Parameter
Estimation in Large-Scale Data | stat.ML | We propose RoBiRank, a ranking algorithm that is motivated by observing a
close connection between evaluation metrics for learning to rank and loss
functions for robust classification. The algorithm shows a very competitive
performance on standard benchmark datasets against other representative
algorithms in the litera... | computer science |
10,769 | Squeezing bottlenecks: exploring the limits of autoencoder semantic
representation capabilities | cs.IR | We present a comprehensive study on the use of autoencoders for modelling
text data, in which (differently from previous studies) we focus our attention
on the following issues: i) we explore the suitability of two different models
bDA and rsDA for constructing deep autoencoders for text data at the sentence
level; ii)... | computer science |
10,770 | Stochastic Gradient Hamiltonian Monte Carlo | stat.ME | Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for
defining distant proposals with high acceptance probabilities in a
Metropolis-Hastings framework, enabling more efficient exploration of the state
space than standard random-walk proposals. The popularity of such methods has
grown significantly in r... | computer science |
10,771 | A Bayesian Model of node interaction in networks | cs.LG | We are concerned with modeling the strength of links in networks by taking
into account how often those links are used. Link usage is a strong indicator
of how closely two nodes are related, but existing network models in Bayesian
Statistics and Machine Learning are able to predict only wether a link exists
at all. As ... | computer science |
10,772 | A convergence proof of the split Bregman method for regularized
least-squares problems | math.OC | The split Bregman (SB) method [T. Goldstein and S. Osher, SIAM J. Imaging
Sci., 2 (2009), pp. 323-43] is a fast splitting-based algorithm that solves
image reconstruction problems with general l1, e.g., total-variation (TV) and
compressed sensing (CS), regularizations by introducing a single variable split
to decouple ... | computer science |
10,773 | Fast X-ray CT image reconstruction using the linearized augmented
Lagrangian method with ordered subsets | math.OC | The augmented Lagrangian (AL) method that solves convex optimization problems
with linear constraints has drawn more attention recently in imaging
applications due to its decomposable structure for composite cost functions and
empirical fast convergence rate under weak conditions. However, for problems
such as X-ray co... | computer science |
10,774 | Incremental Majorization-Minimization Optimization with Application to
Large-Scale Machine Learning | math.OC | Majorization-minimization algorithms consist of successively minimizing a
sequence of upper bounds of the objective function. These upper bounds are
tight at the current estimate, and each iteration monotonically drives the
objective function downhill. Such a simple principle is widely applicable and
has been very popu... | computer science |
10,775 | Retrieval of Experiments by Efficient Estimation of Marginal Likelihood | stat.ML | We study the task of retrieving relevant experiments given a query
experiment. By experiment, we mean a collection of measurements from a set of
`covariates' and the associated `outcomes'. While similar experiments can be
retrieved by comparing available `annotations', this approach ignores the
valuable information ava... | computer science |
10,776 | Efficient Inference of Gaussian Process Modulated Renewal Processes with
Application to Medical Event Data | stat.ML | The episodic, irregular and asynchronous nature of medical data render them
difficult substrates for standard machine learning algorithms. We would like to
abstract away this difficulty for the class of time-stamped categorical
variables (or events) by modeling them as a renewal process and inferring a
probability dens... | computer science |
10,777 | Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse
Optimization and Noisy Matrix Decomposition | cs.LG | We propose an efficient ADMM method with guarantees for high-dimensional
problems. We provide explicit bounds for the sparse optimization problem and
the noisy matrix decomposition problem. For sparse optimization, we establish
that the modified ADMM method has an optimal convergence rate of
$\mathcal{O}(s\log d/T)$, w... | computer science |
10,778 | Pareto-depth for Multiple-query Image Retrieval | cs.IR | Most content-based image retrieval systems consider either one single query,
or multiple queries that include the same object or represent the same semantic
information. In this paper we consider the content-based image retrieval
problem for multiple query images corresponding to different image semantics.
We propose a... | computer science |
10,779 | Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-$1$
Updates | cs.LG | In this paper, we provide local and global convergence guarantees for
recovering CP (Candecomp/Parafac) tensor decomposition. The main step of the
proposed algorithm is a simple alternating rank-$1$ update which is the
alternating version of the tensor power iteration adapted for asymmetric
tensors. Local convergence g... | computer science |
10,780 | Important Molecular Descriptors Selection Using Self Tuned Reweighted
Sampling Method for Prediction of Antituberculosis Activity | cs.LG | In this paper, a new descriptor selection method for selecting an optimal
combination of important descriptors of sulfonamide derivatives data, named
self tuned reweighted sampling (STRS), is developed. descriptors are defined as
the descriptors with large absolute coefficients in a multivariate linear
regression model... | computer science |
10,781 | From Predictive to Prescriptive Analytics | stat.ML | In this paper, we combine ideas from machine learning (ML) and operations
research and management science (OR/MS) in developing a framework, along with
specific methods, for using data to prescribe decisions in OR/MS problems. In a
departure from other work on data-driven optimization and reflecting our
practical exper... | computer science |
10,782 | Semi-Supervised Nonlinear Distance Metric Learning via Forests of
Max-Margin Cluster Hierarchies | stat.ML | Metric learning is a key problem for many data mining and machine learning
applications, and has long been dominated by Mahalanobis methods. Recent
advances in nonlinear metric learning have demonstrated the potential power of
non-Mahalanobis distance functions, particularly tree-based functions. We
propose a novel non... | computer science |
10,783 | Machine Learning Methods in the Computational Biology of Cancer | cs.LG | The objectives of this "perspective" paper are to review some recent advances
in sparse feature selection for regression and classification, as well as
compressed sensing, and to discuss how these might be used to develop tools to
advance personalized cancer therapy. As an illustration of the possibilities, a
new algor... | computer science |
10,784 | Machine Learning at Scale | cs.LG | It takes skill to build a meaningful predictive model even with the abundance
of implementations of modern machine learning algorithms and readily available
computing resources. Building a model becomes challenging if hundreds of
terabytes of data need to be processed to produce the training data set. In a
digital adve... | computer science |
10,785 | Scalable methods for nonnegative matrix factorizations of near-separable
tall-and-skinny matrices | cs.LG | Numerous algorithms are used for nonnegative matrix factorization under the
assumption that the matrix is nearly separable. In this paper, we show how to
make these algorithms efficient for data matrices that have many more rows than
columns, so-called "tall-and-skinny matrices". One key component to these
improved met... | computer science |
10,786 | Multi-period Trading Prediction Markets with Connections to Machine
Learning | cs.GT | We present a new model for prediction markets, in which we use risk measures
to model agents and introduce a market maker to describe the trading process.
This specific choice on modelling tools brings us mathematical convenience. The
analysis shows that the whole market effectively approaches a global objective,
despi... | computer science |
10,787 | Collaborative Filtering with Information-Rich and Information-Sparse
Entities | stat.ML | In this paper, we consider a popular model for collaborative filtering in
recommender systems where some users of a website rate some items, such as
movies, and the goal is to recover the ratings of some or all of the unrated
items of each user. In particular, we consider both the clustering model, where
only users (or... | computer science |
10,788 | A survey of dimensionality reduction techniques | stat.ML | Experimental life sciences like biology or chemistry have seen in the recent
decades an explosion of the data available from experiments. Laboratory
instruments become more and more complex and report hundreds or thousands
measurements for a single experiment and therefore the statistical methods face
challenging tasks... | computer science |
10,789 | Statistical Decision Making for Optimal Budget Allocation in Crowd
Labeling | cs.LG | In crowd labeling, a large amount of unlabeled data instances are outsourced
to a crowd of workers. Workers will be paid for each label they provide, but
the labeling requester usually has only a limited amount of the budget. Since
data instances have different levels of labeling difficulty and workers have
different r... | computer science |
10,790 | Learning the Latent State Space of Time-Varying Graphs | cs.SI | From social networks to Internet applications, a wide variety of electronic
communication tools are producing streams of graph data; where the nodes
represent users and the edges represent the contacts between them over time.
This has led to an increased interest in mechanisms to model the dynamic
structure of time-var... | computer science |
10,791 | Firefly Monte Carlo: Exact MCMC with Subsets of Data | stat.ML | Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose
tool for Bayesian inference. However, MCMC cannot be practically applied to
large data sets because of the prohibitive cost of evaluating every likelihood
term at every iteration. Here we present Firefly Monte Carlo (FlyMC) an
auxiliary variabl... | computer science |
10,792 | Bayesian calibration for forensic evidence reporting | stat.ML | We introduce a Bayesian solution for the problem in forensic speaker
recognition, where there may be very little background material for estimating
score calibration parameters. We work within the Bayesian paradigm of evidence
reporting and develop a principled probabilistic treatment of the problem,
which results in a... | computer science |
10,793 | Variance-Constrained Actor-Critic Algorithms for Discounted and Average
Reward MDPs | cs.LG | In many sequential decision-making problems we may want to manage risk by
minimizing some measure of variability in rewards in addition to maximizing a
standard criterion. Variance related risk measures are among the most common
risk-sensitive criteria in finance and operations research. However, optimizing
many such c... | computer science |
10,794 | DimmWitted: A Study of Main-Memory Statistical Analytics | cs.DB | We perform the first study of the tradeoff space of access methods and
replication to support statistical analytics using first-order methods executed
in the main memory of a Non-Uniform Memory Access (NUMA) machine. Statistical
analytics systems differ from conventional SQL-analytics in the amount and
types of memory ... | computer science |
10,795 | Sharpened Error Bounds for Random Sampling Based $\ell_2$ Regression | cs.LG | Given a data matrix $X \in R^{n\times d}$ and a response vector $y \in
R^{n}$, suppose $n>d$, it costs $O(n d^2)$ time and $O(n d)$ space to solve the
least squares regression (LSR) problem. When $n$ and $d$ are both large,
exactly solving the LSR problem is very expensive. When $n \gg d$, one feasible
approach to spee... | computer science |
10,796 | Sparse K-Means with $\ell_{\infty}/\ell_0$ Penalty for High-Dimensional
Data Clustering | stat.ML | Sparse clustering, which aims to find a proper partition of an extremely
high-dimensional data set with redundant noise features, has been attracted
more and more interests in recent years. The existing studies commonly solve
the problem in a framework of maximizing the weighted feature contributions
subject to a $\ell... | computer science |
10,797 | Learning with incremental iterative regularization | stat.ML | Within a statistical learning setting, we propose and study an iterative
regularization algorithm for least squares defined by an incremental gradient
method. In particular, we show that, if all other parameters are fixed a
priori, the number of passes over the data (epochs) acts as a regularization
parameter, and prov... | computer science |
10,798 | Geodesic Distance Function Learning via Heat Flow on Vector Fields | cs.LG | Learning a distance function or metric on a given data manifold is of great
importance in machine learning and pattern recognition. Many of the previous
works first embed the manifold to Euclidean space and then learn the distance
function. However, such a scheme might not faithfully preserve the distance
function if t... | computer science |
10,799 | Feature selection for classification with class-separability strategy
and data envelopment analysis | cs.LG | In this paper, a novel feature selection method is presented, which is based
on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To
better capture the relationship between features and the class, class labels
are separated into individual variables and relevance and redundancy are
explicitly handle... | computer science |
10,800 | A consistent deterministic regression tree for non-parametric prediction
of time series | math.ST | We study online prediction of bounded stationary ergodic processes. To do so,
we consider the setting of prediction of individual sequences and build a
deterministic regression tree that performs asymptotically as well as the best
L-Lipschitz constant predictors. Then, we show why the obtained regret bound
entails the ... | computer science |
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