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37,909 | Dimension Reduction Using Rule Ensemble Machine Learning Methods: A
Numerical Study of Three Ensemble Methods | stat.ML | Ensemble methods for supervised machine learning have become popular due to
their ability to accurately predict class labels with groups of simple,
lightweight "base learners." While ensembles offer computationally efficient
models that have good predictive capability they tend to be large and offer
little insight into... | computer science |
37,910 | Regularized Laplacian Estimation and Fast Eigenvector Approximation | cs.DS | Recently, Mahoney and Orecchia demonstrated that popular diffusion-based
procedures to compute a quick \emph{approximation} to the first nontrivial
eigenvector of a data graph Laplacian \emph{exactly} solve certain regularized
Semi-Definite Programs (SDPs). In this paper, we extend that result by
providing a statistica... | computer science |
37,911 | Positive definite matrices and the S-divergence | math.FA | Positive definite matrices abound in a dazzling variety of applications. This
ubiquity can be in part attributed to their rich geometric structure: positive
definite matrices form a self-dual convex cone whose strict interior is a
Riemannian manifold. The manifold view is endowed with a "natural" distance
function whil... | computer science |
37,912 | Asymptotically Independent Markov Sampling: a new MCMC scheme for
Bayesian Inference | stat.CO | In Bayesian statistics, many problems can be expressed as the evaluation of
the expectation of a quantity of interest with respect to the posterior
distribution. Standard Monte Carlo method is often not applicable because the
encountered posterior distributions cannot be sampled directly. In this case,
the most popular... | computer science |
37,913 | Readouts for Echo-state Networks Built using Locally Regularized
Orthogonal Forward Regression | stat.ML | Echo state network (ESN) is viewed as a temporal non-orthogonal expansion
with pseudo-random parameters. Such expansions naturally give rise to
regressors of various relevance to a teacher output. We illustrate that often
only a certain amount of the generated echo-regressors effectively explain the
variance of the tea... | computer science |
37,914 | Bayesian Optimization for Adaptive MCMC | stat.CO | This paper proposes a new randomized strategy for adaptive MCMC using
Bayesian optimization. This approach applies to non-differentiable objective
functions and trades off exploration and exploitation to reduce the number of
potentially costly objective function evaluations. We demonstrate the strategy
in the complex s... | computer science |
37,915 | Efficient Marginal Likelihood Computation for Gaussian Process
Regression | stat.ML | In a Bayesian learning setting, the posterior distribution of a predictive
model arises from a trade-off between its prior distribution and the
conditional likelihood of observed data. Such distribution functions usually
rely on additional hyperparameters which need to be tuned in order to achieve
optimum predictive pe... | computer science |
37,916 | Model Selection in Undirected Graphical Models with the Elastic Net | stat.OT | Structure learning in random fields has attracted considerable attention due
to its difficulty and importance in areas such as remote sensing, computational
biology, natural language processing, protein networks, and social network
analysis. We consider the problem of estimating the probabilistic graph
structure associ... | computer science |
37,917 | Vector-valued Reproducing Kernel Banach Spaces with Applications to
Multi-task Learning | math.FA | Motivated by multi-task machine learning with Banach spaces, we propose the
notion of vector-valued reproducing kernel Banach spaces (RKBS). Basic
properties of the spaces and the associated reproducing kernels are
investigated. We also present feature map constructions and several concrete
examples of vector-valued RK... | computer science |
37,918 | Combinatorial clustering and the beta negative binomial process | stat.ME | We develop a Bayesian nonparametric approach to a general family of latent
class problems in which individuals can belong simultaneously to multiple
classes and where each class can be exhibited multiple times by an individual.
We introduce a combinatorial stochastic process known as the negative binomial
process (NBP)... | computer science |
37,919 | Estimation of scale functions to model heteroscedasticity by support
vector machines | stat.ML | A main goal of regression is to derive statistical conclusions on the
conditional distribution of the output variable Y given the input values x. Two
of the most important characteristics of a single distribution are location and
scale. Support vector machines (SVMs) are well established to estimate location
functions ... | computer science |
37,920 | A note on the lack of symmetry in the graphical lasso | stat.ML | The graphical lasso (glasso) is a widely-used fast algorithm for estimating
sparse inverse covariance matrices. The glasso solves an L1 penalized maximum
likelihood problem and is available as an R library on CRAN. The output from
the glasso, a regularized covariance matrix estimate a sparse inverse
covariance matrix e... | computer science |
37,921 | Analog Sparse Approximation with Applications to Compressed Sensing | math.OC | Recent research has shown that performance in signal processing tasks can
often be significantly improved by using signal models based on sparse
representations, where a signal is approximated using a small number of
elements from a fixed dictionary. Unfortunately, inference in this model
involves solving non-smooth op... | computer science |
37,922 | Joint Modeling of Multiple Related Time Series via the Beta Process | stat.ME | We propose a Bayesian nonparametric approach to the problem of jointly
modeling multiple related time series. Our approach is based on the discovery
of a set of latent, shared dynamical behaviors. Using a beta process prior, the
size of the set and the sharing pattern are both inferred from data. We develop
efficient M... | computer science |
37,923 | Krylov Subspace Descent for Deep Learning | stat.ML | In this paper, we propose a second order optimization method to learn models
where both the dimensionality of the parameter space and the number of training
samples is high. In our method, we construct on each iteration a Krylov
subspace formed by the gradient and an approximation to the Hessian matrix, and
then use a ... | computer science |
37,924 | Automatic Relevance Determination in Nonnegative Matrix Factorization
with the β-Divergence | stat.ML | This paper addresses the estimation of the latent dimensionality in
nonnegative matrix factorization (NMF) with the \beta-divergence. The
\beta-divergence is a family of cost functions that includes the squared
Euclidean distance, Kullback-Leibler and Itakura-Saito divergences as special
cases. Learning the model order... | computer science |
37,925 | Extension of SBL Algorithms for the Recovery of Block Sparse Signals
with Intra-Block Correlation | stat.ML | We examine the recovery of block sparse signals and extend the framework in
two important directions; one by exploiting signals' intra-block correlation
and the other by generalizing signals' block structure. We propose two families
of algorithms based on the framework of block sparse Bayesian learning (BSBL).
One fami... | computer science |
37,926 | Split HMC for Gaussian Process Models | stat.CO | In this paper, we discuss an extension of the Split Hamiltonian Monte Carlo
(Split HMC) method for Gaussian process model (GPM). This method is based on
splitting the Hamiltonian in a way that allows much of the movement around the
state space to be done at low computational cost. To this end, we approximate
the negati... | computer science |
37,927 | Dynamic trees for streaming and massive data contexts | stat.ME | Data collection at a massive scale is becoming ubiquitous in a wide variety
of settings, from vast offline databases to streaming real-time information.
Learning algorithms deployed in such contexts must rely on single-pass
inference, where the data history is never revisited. In streaming contexts,
learning must also ... | computer science |
37,928 | Approximate Computation and Implicit Regularization for Very Large-scale
Data Analysis | cs.DS | Database theory and database practice are typically the domain of computer
scientists who adopt what may be termed an algorithmic perspective on their
data. This perspective is very different than the more statistical perspective
adopted by statisticians, scientific computers, machine learners, and other who
work on wh... | computer science |
37,929 | Sequential Design for Computer Experiments with a Flexible Bayesian
Additive Model | stat.ME | In computer experiments, a mathematical model implemented on a computer is
used to represent complex physical phenomena. These models, known as computer
simulators, enable experimental study of a virtual representation of the
complex phenomena. Simulators can be thought of as complex functions that take
many inputs and... | computer science |
37,930 | A Short Note on Gaussian Process Modeling for Large Datasets using
Graphics Processing Units | stat.CO | The graphics processing unit (GPU) has emerged as a powerful and cost
effective processor for general performance computing. GPUs are capable of an
order of magnitude more floating-point operations per second as compared to
modern central processing units (CPUs), and thus provide a great deal of
promise for computation... | computer science |
37,931 | Selection of tuning parameters in bridge regression models via Bayesian
information criterion | stat.ME | We consider the bridge linear regression modeling, which can produce a sparse
or non-sparse model. A crucial point in the model building process is the
selection of adjusted parameters including a regularization parameter and a
tuning parameter in bridge regression models. The choice of the adjusted
parameters can be v... | computer science |
37,932 | Semi-blind Sparse Image Reconstruction with Application to MRFM | stat.ML | We propose a solution to the image deconvolution problem where the
convolution kernel or point spread function (PSF) is assumed to be only
partially known. Small perturbations generated from the model are exploited to
produce a few principal components explaining the PSF uncertainty in a high
dimensional space. Unlike ... | computer science |
37,933 | Corrected Kriging update formulae for batch-sequential data assimilation | stat.ML | Recently, a lot of effort has been paid to the efficient computation of
Kriging predictors when observations are assimilated sequentially. In
particular, Kriging update formulae enabling significant computational savings
were derived in Barnes and Watson (1992), Gao et al. (1996), and Emery (2009).
Taking advantage of ... | computer science |
37,934 | Convergence Properties of Kronecker Graphical Lasso Algorithms | stat.ME | This paper studies iteration convergence of Kronecker graphical lasso
(KGLasso) algorithms for estimating the covariance of an i.i.d. Gaussian random
sample under a sparse Kronecker-product covariance model and MSE convergence
rates. The KGlasso model, originally called the transposable regularized
covariance model by ... | computer science |
37,935 | Distributed Robust Power System State Estimation | stat.ML | Deregulation of energy markets, penetration of renewables, advanced metering
capabilities, and the urge for situational awareness, all call for system-wide
power system state estimation (PSSE). Implementing a centralized estimator
though is practically infeasible due to the complexity scale of an
interconnection, the c... | computer science |
37,936 | Optimally-Weighted Herding is Bayesian Quadrature | stat.ML | Herding and kernel herding are deterministic methods of choosing samples
which summarise a probability distribution. A related task is choosing samples
for estimating integrals using Bayesian quadrature. We show that the criterion
minimised when selecting samples in kernel herding is equivalent to the
posterior varianc... | computer science |
37,937 | Balancing Lifetime and Classification Accuracy of Wireless Sensor
Networks | cs.NI | Wireless sensor networks are composed of distributed sensors that can be used
for signal detection or classification. The likelihood functions of the
hypotheses are often not known in advance, and decision rules have to be
learned via supervised learning. A specific such algorithm is Fisher
discriminant analysis (FDA),... | computer science |
37,938 | Efficient Algorithm for Extremely Large Multi-task Regression with
Massive Structured Sparsity | stat.ML | We develop a highly scalable optimization method called "hierarchical
group-thresholding" for solving a multi-task regression model with complex
structured sparsity constraints on both input and output spaces. Despite the
recent emergence of several efficient optimization algorithms for tackling
complex sparsity-induci... | computer science |
37,939 | Consistent selection of tuning parameters via variable selection
stability | stat.ML | Penalized regression models are popularly used in high-dimensional data
analysis to conduct variable selection and model fitting simultaneously.
Whereas success has been widely reported in literature, their performances
largely depend on the tuning parameters that balance the trade-off between
model fitting and model s... | computer science |
37,940 | Minerva and minepy: a C engine for the MINE suite and its R, Python and
MATLAB wrappers | stat.ML | We introduce a novel implementation in ANSI C of the MINE family of
algorithms for computing maximal information-based measures of dependence
between two variables in large datasets, with the aim of a low memory footprint
and ease of integration within bioinformatics pipelines. We provide the
libraries minerva (with th... | computer science |
37,941 | Multiresolution Gaussian Processes | stat.ME | We propose a multiresolution Gaussian process to capture long-range,
non-Markovian dependencies while allowing for abrupt changes. The
multiresolution GP hierarchically couples a collection of smooth GPs, each
defined over an element of a random nested partition. Long-range dependencies
are captured by the top-level GP... | computer science |
37,942 | Augment-and-Conquer Negative Binomial Processes | stat.ML | By developing data augmentation methods unique to the negative binomial (NB)
distribution, we unite seemingly disjoint count and mixture models under the NB
process framework. We develop fundamental properties of the models and derive
efficient Gibbs sampling inference. We show that the gamma-NB process can be
reduced ... | computer science |
37,943 | Restricting exchangeable nonparametric distributions | stat.ME | Distributions over exchangeable matrices with infinitely many columns, such
as the Indian buffet process, are useful in constructing nonparametric latent
variable models. However, the distribution implied by such models over the
number of features exhibited by each data point may be poorly- suited for many
modeling tas... | computer science |
37,944 | Group descent algorithms for nonconvex penalized linear and logistic
regression models with grouped predictors | stat.CO | Penalized regression is an attractive framework for variable selection
problems. Often, variables possess a grouping structure, and the relevant
selection problem is that of selecting groups, not individual variables. The
group lasso has been proposed as a way of extending the ideas of the lasso to
the problem of group... | computer science |
37,945 | Positivity and Transportation | stat.ML | We prove in this paper that the weighted volume of the set of integral
transportation matrices between two integral histograms r and c of equal sum is
a positive definite kernel of r and c when the set of considered weights forms
a positive definite matrix. The computation of this quantity, despite being the
subject of... | computer science |
37,946 | Signal Recovery in Unions of Subspaces with Applications to Compressive
Imaging | stat.ML | In applications ranging from communications to genetics, signals can be
modeled as lying in a union of subspaces. Under this model, signal coefficients
that lie in certain subspaces are active or inactive together. The potential
subspaces are known in advance, but the particular set of subspaces that are
active (i.e., ... | computer science |
37,947 | Negative Binomial Process Count and Mixture Modeling | stat.ME | The seemingly disjoint problems of count and mixture modeling are united
under the negative binomial (NB) process. A gamma process is employed to model
the rate measure of a Poisson process, whose normalization provides a random
probability measure for mixture modeling and whose marginalization leads to an
NB process f... | computer science |
37,948 | Probabilistic Auto-Associative Models and Semi-Linear PCA | stat.AP | Auto-Associative models cover a large class of methods used in data analysis.
In this paper, we describe the generals properties of these models when the
projection component is linear and we propose and test an easy to implement
Probabilistic Semi-Linear Auto- Associative model in a Gaussian setting. We
show it is a g... | computer science |
37,949 | A Note on the SPICE Method | stat.ML | In this article, we analyze the SPICE method developed in [1], and establish
its connections with other standard sparse estimation methods such as the Lasso
and the LAD-Lasso. This result positions SPICE as a computationally efficient
technique for the calculation of Lasso-type estimators. Conversely, this
connection i... | computer science |
37,950 | Validation of Soft Classification Models using Partial Class
Memberships: An Extended Concept of Sensitivity & Co. applied to the Grading
of Astrocytoma Tissues | stat.ML | We use partial class memberships in soft classification to model uncertain
labelling and mixtures of classes. Partial class memberships are not restricted
to predictions, but may also occur in reference labels (ground truth, gold
standard diagnosis) for training and validation data.
Classifier performance is usually ... | computer science |
37,951 | Reconstructing Self Organizing Maps as Spider Graphs for better visual
interpretation of large unstructured datasets | cs.GR | Self-Organizing Maps (SOM) are popular unsupervised artificial neural network
used to reduce dimensions and visualize data. Visual interpretation from
Self-Organizing Maps (SOM) has been limited due to grid approach of data
representation, which makes inter-scenario analysis impossible. The paper
proposes a new way to ... | computer science |
37,952 | A Geometric Blind Source Separation Method Based on Facet Component
Analysis | math.NA | Given a set of mixtures, blind source separation attempts to retrieve the
source signals without or with very little information of the the mixing
process. We present a geometric approach for blind separation of nonnegative
linear mixtures termed {\em facet component analysis} (FCA). The approach is
based on facet iden... | computer science |
37,953 | A proximal Newton framework for composite minimization: Graph learning
without Cholesky decompositions and matrix inversions | stat.ML | We propose an algorithmic framework for convex minimization problems of a
composite function with two terms: a self-concordant function and a possibly
nonsmooth regularization term. Our method is a new proximal Newton algorithm
that features a local quadratic convergence rate. As a specific instance of our
framework, w... | computer science |
37,954 | Fano schemes of generic intersections and machine learning | math.AG | We investigate Fano schemes of conditionally generic intersections, i.e. of
hypersurfaces in projective space chosen generically up to additional
conditions. Via a correspondence between generic properties of algebraic
varieties and events in probability spaces that occur with probability one, we
use the obtained resul... | computer science |
37,955 | Anomaly Classification with the Anti-Profile Support Vector Machine | stat.ML | We introduce the anti-profile Support Vector Machine (apSVM) as a novel
algorithm to address the anomaly classification problem, an extension of
anomaly detection where the goal is to distinguish data samples from a number
of anomalous and heterogeneous classes based on their pattern of deviation from
a normal stable c... | computer science |
37,956 | Supervised Classification Using Sparse Fisher's LDA | stat.ML | It is well known that in a supervised classification setting when the number
of features is smaller than the number of observations, Fisher's linear
discriminant rule is asymptotically Bayes. However, there are numerous modern
applications where classification is needed in the high-dimensional setting.
Naive implementa... | computer science |
37,957 | Explorative Data Analysis for Changes in Neural Activity | stat.ML | Neural recordings are nonstationary time series, i.e. their properties
typically change over time. Identifying specific changes, e.g. those induced by
a learning task, can shed light on the underlying neural processes. However,
such changes of interest are often masked by strong unrelated changes, which
can be of physi... | computer science |
37,958 | An Extragradient-Based Alternating Direction Method for Convex
Minimization | math.OC | In this paper, we consider the problem of minimizing the sum of two convex
functions subject to linear linking constraints. The classical alternating
direction type methods usually assume that the two convex functions have
relatively easy proximal mappings. However, many problems arising from
statistics, image processi... | computer science |
37,959 | A note on selection stability: combining stability and prediction | stat.ME | Recently, many regularized procedures have been proposed for variable
selection in linear regression, but their performance depends on the tuning
parameter selection. Here a criterion for the tuning parameter selection is
proposed, which combines the strength of both stability selection and
cross-validation and therefo... | computer science |
37,960 | An improved quasar detection method in EROS-2 and MACHO LMC datasets | stat.ML | We present a new classification method for quasar identification in the
EROS-2 and MACHO datasets based on a boosted version of Random Forest
classifier. We use a set of variability features including parameters of a
continuous auto regressive model. We prove that continuous auto regressive
parameters are very importan... | computer science |
37,961 | High-dimensional Mixed Graphical Models | stat.ML | While graphical models for continuous data (Gaussian graphical models) and
discrete data (Ising models) have been extensively studied, there is little
work on graphical models linking both continuous and discrete variables (mixed
data), which are common in many scientific applications. We propose a novel
graphical mode... | computer science |
37,962 | Identifying cancer subtypes in glioblastoma by combining genomic,
transcriptomic and epigenomic data | stat.ML | We present a nonparametric Bayesian method for disease subtype discovery in
multi-dimensional cancer data. Our method can simultaneously analyse a wide
range of data types, allowing for both agreement and disagreement between their
underlying clustering structure. It includes feature selection and infers the
most likel... | computer science |
37,963 | A Counterexample for the Validity of Using Nuclear Norm as a Convex
Surrogate of Rank | stat.ML | Rank minimization has attracted a lot of attention due to its robustness in
data recovery. To overcome the computational difficulty, rank is often replaced
with nuclear norm. For several rank minimization problems, such a replacement
has been theoretically proven to be valid, i.e., the solution to nuclear norm
minimiza... | computer science |
37,964 | Comparison of several reweighted l1-algorithms for solving cardinality
minimization problems | math.OC | Reweighted l1-algorithms have attracted a lot of attention in the field of
applied mathematics. A unified framework of such algorithms has been recently
proposed by Zhao and Li. In this paper we construct a few new examples of
reweighted l1-methods. These functions are certain concave approximations of
the l0-norm func... | computer science |
37,965 | Declarative Modeling and Bayesian Inference of Dark Matter Halos | stat.ML | Probabilistic programming allows specification of probabilistic models in a
declarative manner. Recently, several new software systems and languages for
probabilistic programming have been developed on the basis of newly developed
and improved methods for approximate inference in probabilistic models. In this
contribut... | computer science |
37,966 | Particle approximations of the score and observed information matrix for
parameter estimation in state space models with linear computational cost | stat.CO | Poyiadjis et al. (2011) show how particle methods can be used to estimate
both the score and the observed information matrix for state space models.
These methods either suffer from a computational cost that is quadratic in the
number of particles, or produce estimates whose variance increases
quadratically with the am... | computer science |
37,967 | A Kernel Test for Three-Variable Interactions | stat.ME | We introduce kernel nonparametric tests for Lancaster three-variable
interaction and for total independence, using embeddings of signed measures
into a reproducing kernel Hilbert space. The resulting test statistics are
straightforward to compute, and are used in powerful interaction tests, which
are consistent against... | computer science |
37,968 | Unsupervised deconvolution of dynamic imaging reveals intratumor
vascular heterogeneity | stat.ML | Intratumor heterogeneity is often manifested by vascular compartments with
distinct pharmacokinetics that cannot be resolved directly by in vivo dynamic
imaging. We developed tissue-specific compartment modeling (TSCM), an
unsupervised computational method of deconvolving dynamic imaging series from
heterogeneous tumor... | computer science |
37,969 | Bayesian test of significance for conditional independence: The
multinomial model | stat.CO | Conditional independence tests (CI tests) have received special attention
lately in Machine Learning and Computational Intelligence related literature as
an important indicator of the relationship among the variables used by their
models. In the field of Probabilistic Graphical Models (PGM)--which includes
Bayesian Net... | computer science |
37,970 | Local case-control sampling: Efficient subsampling in imbalanced data
sets | stat.CO | For classification problems with significant class imbalance, subsampling can
reduce computational costs at the price of inflated variance in estimating
model parameters. We propose a method for subsampling efficiently for logistic
regression by adjusting the class balance locally in feature space via an
accept-reject ... | computer science |
37,971 | Supersparse Linear Integer Models for Interpretable Classification | stat.ML | Scoring systems are classification models that only require users to add,
subtract and multiply a few meaningful numbers to make a prediction. These
models are often used because they are practical and interpretable. In this
paper, we introduce an off-the-shelf tool to create scoring systems that both
accurate and inte... | computer science |
37,972 | Foundations of a Multi-way Spectral Clustering Framework for Hybrid
Linear Modeling | stat.ML | The problem of Hybrid Linear Modeling (HLM) is to model and segment data
using a mixture of affine subspaces. Different strategies have been proposed to
solve this problem, however, rigorous analysis justifying their performance is
missing. This paper suggests the Theoretical Spectral Curvature Clustering
(TSCC) algori... | computer science |
37,973 | Gibbs posterior for variable selection in high-dimensional
classification and data mining | stat.ME | In the popular approach of "Bayesian variable selection" (BVS), one uses
prior and posterior distributions to select a subset of candidate variables to
enter the model. A completely new direction will be considered here to study
BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs
posterior is con... | computer science |
37,974 | Tuning parameter selection for penalized likelihood estimation of
inverse covariance matrix | stat.ME | In a Gaussian graphical model, the conditional independence between two
variables are characterized by the corresponding zero entries in the inverse
covariance matrix. Maximum likelihood method using the smoothly clipped
absolute deviation (SCAD) penalty (Fan and Li, 2001) and the adaptive LASSO
penalty (Zou, 2006) hav... | computer science |
37,975 | High-dimensional Graphical Model Search with gRapHD R Package | stat.ML | This paper presents the R package gRapHD for efficient selection of
high-dimensional undirected graphical models. The package provides tools for
selecting trees, forests and decomposable models minimizing information
criteria such as AIC or BIC, and for displaying the independence graphs of the
models. It has also some... | computer science |
37,976 | On Ranking Senators By Their Votes | stat.ML | The problem of ranking a set of objects given some measure of similarity is
one of the most basic in machine learning. Recently Agarwal proposed a method
based on techniques in semi-supervised learning utilizing the graph Laplacian.
In this work we consider a novel application of this technique to ranking
binary choice... | computer science |
37,977 | A Nonconformity Approach to Model Selection for SVMs | stat.ML | We investigate the issue of model selection and the use of the nonconformity
(strangeness) measure in batch learning. Using the nonconformity measure we
propose a new training algorithm that helps avoid the need for Cross-Validation
or Leave-One-Out model selection strategies. We provide a new generalisation
error boun... | computer science |
37,978 | Computing p-values of LiNGAM outputs via Multiscale Bootstrap | stat.ML | Structural equation models and Bayesian networks have been widely used to
study causal relationships between continuous variables. Recently, a
non-Gaussian method called LiNGAM was proposed to discover such causal models
and has been extended in various directions. An important problem with LiNGAM
is that the results a... | computer science |
37,979 | Rumors in a Network: Who's the Culprit? | stat.ML | We provide a systematic study of the problem of finding the source of a rumor
in a network. We model rumor spreading in a network with a variant of the
popular SIR model and then construct an estimator for the rumor source. This
estimator is based upon a novel topological quantity which we term
\textbf{rumor centrality... | computer science |
37,980 | SpicyMKL | stat.ML | We propose a new optimization algorithm for Multiple Kernel Learning (MKL)
called SpicyMKL, which is applicable to general convex loss functions and
general types of regularization. The proposed SpicyMKL iteratively solves
smooth minimization problems. Thus, there is no need of solving SVM, LP, or QP
internally. SpicyM... | computer science |
37,981 | A Variational Bayes Approach to Decoding in a Phase-Uncertain Digital
Receiver | stat.AP | This paper presents a Bayesian approach to symbol and phase inference in a
phase-unsynchronized digital receiver. It primarily extends [Quinn 2011] to the
multi-symbol case, using the variational Bayes (VB) approximation to deal with
the combinatorial complexity of the phase inference in this case. The work
provides a ... | computer science |
37,982 | Iteration Complexity of Randomized Block-Coordinate Descent Methods for
Minimizing a Composite Function | math.OC | In this paper we develop a randomized block-coordinate descent method for
minimizing the sum of a smooth and a simple nonsmooth block-separable convex
function and prove that it obtains an $\epsilon$-accurate solution with
probability at least $1-\rho$ in at most $O(\tfrac{n}{\epsilon} \log
\tfrac{1}{\rho})$ iterations... | computer science |
37,983 | Multi-Task Averaging | stat.ML | We present a multi-task learning approach to jointly estimate the means of
multiple independent data sets. The proposed multi-task averaging (MTA)
algorithm results in a convex combination of the single-task maximum likelihood
estimates. We derive the optimal minimum risk estimator and the minimax
estimator, and show t... | computer science |
37,984 | Generalized Beta Mixtures of Gaussians | stat.ME | In recent years, a rich variety of shrinkage priors have been proposed that
have great promise in addressing massive regression problems. In general, these
new priors can be expressed as scale mixtures of normals, but have more complex
forms and better properties than traditional Cauchy and double exponential
priors. W... | computer science |
37,985 | Expectation-Propagation for Likelihood-Free Inference | stat.CO | Many models of interest in the natural and social sciences have no
closed-form likelihood function, which means that they cannot be treated using
the usual techniques of statistical inference. In the case where such models
can be efficiently simulated, Bayesian inference is still possible thanks to
the Approximate Baye... | computer science |
37,986 | Adaptive Independent Sticky MCMC algorithms | stat.CO | In this work, we introduce a novel class of adaptive Monte Carlo methods,
called adaptive independent sticky MCMC algorithms, for efficient sampling from
a generic target probability density function (pdf). The new class of
algorithms employs adaptive non-parametric proposal densities which become
closer and closer to ... | computer science |
37,987 | Joint modeling of multiple time series via the beta process with
application to motion capture segmentation | stat.ME | We propose a Bayesian nonparametric approach to the problem of jointly
modeling multiple related time series. Our model discovers a latent set of
dynamical behaviors shared among the sequences, and segments each time series
into regions defined by a subset of these behaviors. Using a beta process
prior, the size of the... | computer science |
37,988 | Compound Poisson Processes, Latent Shrinkage Priors and Bayesian
Nonconvex Penalization | stat.ML | In this paper we discuss Bayesian nonconvex penalization for sparse learning
problems. We explore a nonparametric formulation for latent shrinkage
parameters using subordinators which are one-dimensional L\'{e}vy processes. We
particularly study a family of continuous compound Poisson subordinators and a
family of disc... | computer science |
37,989 | Multiscale Inference for High-Frequency Data | stat.ME | This paper proposes a novel multiscale estimator for the integrated
volatility of an Ito process, in the presence of market microstructure noise
(observation error). The multiscale structure of the observed process is
represented frequency-by-frequency and the concept of the multiscale ratio is
introduced to quantify t... | computer science |
37,990 | New probabilistic interest measures for association rules | cs.DB | Mining association rules is an important technique for discovering meaningful
patterns in transaction databases. Many different measures of interestingness
have been proposed for association rules. However, these measures fail to take
the probabilistic properties of the mined data into account. In this paper, we
start ... | computer science |
37,991 | PDE-Foam - a probability-density estimation method using self-adapting
phase-space binning | stat.ML | Probability Density Estimation (PDE) is a multivariate discrimination
technique based on sampling signal and background densities defined by event
samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase
space. In this paper, we present a modification of the PDE method that uses a
self-adapting bi... | computer science |
37,992 | Ultrahigh dimensional variable selection: beyond the linear model | stat.ME | Variable selection in high-dimensional space characterizes many contemporary
problems in scientific discovery and decision making. Many frequently-used
techniques are based on independence screening; examples include correlation
ranking (Fan and Lv, 2008) or feature selection using a two-sample t-test in
high-dimension... | computer science |
37,993 | Feature selection in omics prediction problems using cat scores and
false nondiscovery rate control | stat.AP | We revisit the problem of feature selection in linear discriminant analysis
(LDA), that is, when features are correlated. First, we introduce a pooled
centroids formulation of the multiclass LDA predictor function, in which the
relative weights of Mahalanobis-transformed predictors are given by
correlation-adjusted $t$... | computer science |
37,994 | Regularization methods for learning incomplete matrices | stat.ML | We use convex relaxation techniques to provide a sequence of solutions to the
matrix completion problem. Using the nuclear norm as a regularizer, we provide
simple and very efficient algorithms for minimizing the reconstruction error
subject to a bound on the nuclear norm. Our algorithm iteratively replaces the
missing... | computer science |
37,995 | High Dimensional Nonlinear Learning using Local Coordinate Coding | stat.ML | This paper introduces a new method for semi-supervised learning on high
dimensional nonlinear manifolds, which includes a phase of unsupervised basis
learning and a phase of supervised function learning. The learned bases provide
a set of anchor points to form a local coordinate system, such that each data
point $x$ on... | computer science |
37,996 | Multiple Hypothesis Testing in Pattern Discovery | stat.ML | The problem of multiple hypothesis testing arises when there are more than
one hypothesis to be tested simultaneously for statistical significance. This
is a very common situation in many data mining applications. For instance,
assessing simultaneously the significance of all frequent itemsets of a single
dataset entai... | computer science |
37,997 | A path algorithm for the Fused Lasso Signal Approximator | stat.CO | The Lasso is a very well known penalized regression model, which adds an
$L_{1}$ penalty with parameter $\lambda_{1}$ on the coefficients to the squared
error loss function. The Fused Lasso extends this model by also putting an
$L_{1}$ penalty with parameter $\lambda_{2}$ on the difference of neighboring
coefficients, ... | computer science |
37,998 | BRAINSTORMING: Consensus Learning in Practice | stat.ML | We present here an introduction to Brainstorming approach, that was recently
proposed as a consensus meta-learning technique, and used in several practical
applications in bioinformatics and chemoinformatics. The consensus learning
denotes heterogeneous theoretical classification method, where one trains an
ensemble of... | computer science |
37,999 | Distance Dependent Chinese Restaurant Processes | stat.ML | We develop the distance dependent Chinese restaurant process (CRP), a
flexible class of distributions over partitions that allows for
non-exchangeability. This class can be used to model many kinds of dependencies
between data in infinite clustering models, including dependencies across time
or space. We examine the pr... | computer science |
38,000 | Fast Robust Methods for Singular State-Space Models | math.OC | State-space models are used in a wide range of time series analysis
formulations. Kalman filtering and smoothing are work-horse algorithms in these
settings. While classic algorithms assume Gaussian errors to simplify
estimation, recent advances use a broader range of optimization formulations to
allow outlier-robust e... | computer science |
38,001 | Aggregation using input-output trade-off | stat.ML | In this paper, we introduce a new learning strategy based on a seminal idea
of Mojirsheibani (1999, 2000, 2002a, 2002b), who proposed a smart method for
combining several classifiers, relying on a consensus notion. In many
aggregation methods, the prediction for a new observation x is computed by
building a linear or c... | computer science |
38,002 | Robust MCMC Sampling with Non-Gaussian and Hierarchical Priors in High
Dimensions | stat.ME | A key problem in inference for high dimensional unknowns is the design of
sampling algorithms whose performance scales favourably with the dimension of
the unknown. A typical setting in which these problems arise is the area of
Bayesian inverse problems. In such problems, which include graph-based
learning, nonparametr... | computer science |
38,003 | A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex
Optimization | math.OC | In this work, we present a globalized stochastic semismooth Newton method for
solving stochastic optimization problems involving smooth nonconvex and
nonsmooth convex terms in the objective function. We assume that only noisy
gradient and Hessian information of the smooth part of the objective function
is available via... | computer science |
38,004 | Machine Learning Harnesses Molecular Dynamics to Discover New $μ$
Opioid Chemotypes | stat.ML | Computational chemists typically assay drug candidates by virtually screening
compounds against crystal structures of a protein despite the fact that some
targets, like the $\mu$ Opioid Receptor and other members of the GPCR family,
traverse many non-crystallographic states. We discover new conformational
states of $\m... | computer science |
38,005 | Coregionalised Locomotion Envelopes - A Qualitative Approach | stat.ML | 'Sharing of statistical strength' is a phrase often employed in machine
learning and signal processing. In sensor networks, for example, missing
signals from certain sensors may be predicted by exploiting their correlation
with observed signals acquired from other sensors. For humans, our hands move
synchronously with ... | computer science |
38,006 | Coordination via predictive assistants from a game-theoretic view | cs.GT | We study machine learning-based assistants that support coordination between
humans in congested facilities via congestion forecasts. In our theoretical
analysis, we use game theory to study how an assistant's forecast that
influences the outcome relates to Nash equilibria, and how they can be reached
quickly in conges... | computer science |
38,007 | A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application
to Power Forecasting for Renewable Energy Generation | stat.AP | In this article, we propose a novel ensemble technique with a multi-scheme
weighting based on a technique called coopetitive soft gating. This technique
combines both, ensemble member competition and cooperation, in order to
maximize the overall forecasting accuracy of the ensemble. The proposed
algorithm combines the ... | computer science |
38,008 | Evaluating Conditional Cash Transfer Policies with Machine Learning
Methods | econ.EM | This paper presents an out-of-sample prediction comparison between major
machine learning models and the structural econometric model. Over the past
decade, machine learning has established itself as a powerful tool in many
prediction applications, but this approach is still not widely adopted in
empirical economic stu... | computer science |
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