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Abstract: We present a novel Bayesian method for the joint reconstruction of cosmological matter density fields, peculiar velocities and power-spectra in the quasi-nonlinear regime. We study its applicability to the Ly-alpha forest based on multiple quasar absorption spectra. Our approach to this problem includes a mul... |
Title: Stochastic Approximation and Modern Model-Based Designs for Dose-Finding Clinical Trials |
Abstract: In 1951 Robbins and Monro published the seminal article on stochastic approximation and made a specific reference to its application to the "estimation of a quantal using response, nonresponse data." Since the 1990s, statistical methodology for dose-finding studies has grown into an active area of research. T... |
Title: Continual Reassessment and Related Dose-Finding Designs |
Abstract: During the last twenty years there have been considerable methodological developments in the design and analysis of Phase 1, Phase 2 and Phase 1/2 dose-finding studies. Many of these developments are related to the continual reassessment method (CRM), first introduced by O'Quigley, Pepe and Fisher (\citeyearQ... |
Title: Nuclear norm penalization and optimal rates for noisy low rank matrix completion |
Abstract: This paper deals with the trace regression model where $n$ entries or linear combinations of entries of an unknown $m_1\times m_2$ matrix $A_0$ corrupted by noise are observed. We propose a new nuclear norm penalized estimator of $A_0$ and establish a general sharp oracle inequality for this estimator for arb... |
Title: Nonparametric Bayesian sparse factor models with application to gene expression modeling |
Abstract: A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $$ is modeled as a linear superposition, $$, of a potentially infinite number of hidden factors, $$. The Indian Buffet Process (IBP) is used as a prior on $$ to incorporate sparsity and to allow the number of latent fea... |
Title: Geometric kernel smoothing of tensor fields |
Abstract: In this paper, we study a kernel smoothing approach for denoising a tensor field. Particularly, both simulation studies and theoretical analysis are conducted to understand the effects of the noise structure and the structure of the tensor field on the performance of different smoothers arising from using dif... |
Title: New Null Space Results and Recovery Thresholds for Matrix Rank Minimization |
Abstract: Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimization problems. Similar to compressed sensing, using null space characterizations, recovery thresholds for NNM have been studied in . However simulations show that the thresholds are far from optimal, especial... |
Title: A coordinate-wise optimization algorithm for the Fused Lasso |
Abstract: L1 -penalized regression methods such as the Lasso (Tibshirani 1996) that achieve both variable selection and shrinkage have been very popular. An extension of this method is the Fused Lasso (Tibshirani and Wang 2007), which allows for the incorporation of external information into the model. In this article,... |
Title: Testing for tail behavior using extreme spacings |
Abstract: Methodologies to test hypotheses about the tail-heaviness of an underlying distribution are introduced based on results of Rojo (1996) using the limiting behavior of the extreme spacings. The tests are consistent and have point-wise robust levels in the sense of Lehmann (2005) and Lehmann and Loh (1990). Simu... |
Title: Dose Finding with Escalation with Overdose Control (EWOC) in Cancer Clinical Trials |
Abstract: Traditionally, the major objective in phase I trials is to identify a working-dose for subsequent studies, whereas the major endpoint in phase II and III trials is treatment efficacy. The dose sought is typically referred to as the maximum tolerated dose (MTD). Several statistical methodologies have been prop... |
Title: Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials |
Abstract: An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called "phase I" or "phase I/II" trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based ... |
Title: Approximate Dynamic Programming and Its Applications to the Design of Phase I Cancer Trials |
Abstract: Optimal design of a Phase I cancer trial can be formulated as a stochastic optimization problem. By making use of recent advances in approximate dynamic programming to tackle the problem, we develop an approximation of the Bayesian optimal design. The resulting design is a convex combination of a "treatment" ... |
Title: Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models |
Abstract: Non-gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The sp... |
Title: Learning sparse representations of depth |
Abstract: This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically o... |
Title: Counting in Graph Covers: A Combinatorial Characterization of the Bethe Entropy Function |
Abstract: We present a combinatorial characterization of the Bethe entropy function of a factor graph, such a characterization being in contrast to the original, analytical, definition of this function. We achieve this combinatorial characterization by counting valid configurations in finite graph covers of the factor ... |
Title: Posterior model probabilities computed from model-specific Gibbs output |
Abstract: Reversible jump Markov chain Monte Carlo (RJMCMC) extends ordinary MCMC methods for use in Bayesian multimodel inference. We show that RJMCMC can be implemented as Gibbs sampling with alternating updates of a model indicator and a vector-valued "palette" of parameters denoted $\bm \psi$. Like an artist uses t... |
Title: Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence |
Abstract: Gesture recognition is mainly apprehensive on analyzing the functionality of human wits. The main goal of gesture recognition is to create a system which can recognize specific human gestures and use them to convey information or for device control. Hand gestures provide a separate complementary modality to s... |
Title: An Effective Method of Image Retrieval using Image Mining Techniques |
Abstract: The present research scholars are having keen interest in doing their research activities in the area of Data mining all over the world. Especially, [13]Mining Image data is the one of the essential features in this present scenario since image data plays vital role in every aspect of the system such as busin... |
Title: Temporal and Spatial Independent Component Analysis for fMRI data sets embedded in a R package |
Abstract: For statistical analysis of functional Magnetic Resonance Imaging (fMRI) data sets, we propose a data-driven approach based on Independent Component Analysis (ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spati... |
Title: A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems |
Abstract: Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty... |
Title: Conjugate Projective Limits |
Abstract: We characterize conjugate nonparametric Bayesian models as projective limits of conjugate, finite-dimensional Bayesian models. In particular, we identify a large class of nonparametric models representable as infinite-dimensional analogues of exponential family distributions and their canonical conjugate prio... |
Title: A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms |
Abstract: Recent years have witnessed the popularity of using rank minimization as a regularizer for various signal processing and machine learning problems. As rank minimization problems are often converted to nuclear norm minimization (NNM) problems, they have to be solved iteratively and each iteration requires comp... |
Title: Optimal measures and Markov transition kernels |
Abstract: We study optimal solutions to an abstract optimization problem for measures, which is a generalization of classical variational problems in information theory and statistical physics. In the classical problems, information and relative entropy are defined using the Kullback-Leibler divergence, and for this re... |
Title: Estimating Probabilities in Recommendation Systems |
Abstract: Recommendation systems are emerging as an important business application with significant economic impact. Currently popular systems include Amazon's book recommendations, Netflix's movie recommendations, and Pandora's music recommendations. In this paper we address the problem of estimating probabilities ass... |
Title: Agnostic Learning of Monomials by Halfspaces is Hard |
Abstract: We prove the following strong hardness result for learning: Given a distribution of labeled examples from the hypercube such that there exists a monomial consistent with $(1-\eps)$ of the examples, it is NP-hard to find a halfspace that is correct on $(1/2+\eps)$ of the examples, for arbitrary constants $\eps... |
Title: Closed-set-based Discovery of Bases of Association Rules |
Abstract: The output of an association rule miner is often huge in practice. This is why several concise lossless representations have been proposed, such as the "essential" or "representative" rules. We revisit the algorithm given by Kryszkiewicz (Int. Symp. Intelligent Data Analysis 2001, Springer-Verlag LNCS 2189, 3... |
Title: Border Algorithms for Computing Hasse Diagrams of Arbitrary Lattices |
Abstract: The Border algorithm and the iPred algorithm find the Hasse diagrams of FCA lattices. We show that they can be generalized to arbitrary lattices. In the case of iPred, this requires the identification of a join-semilattice homomorphism into a distributive lattice. |
Title: An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA |
Abstract: Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding critical points of a quadratic function subject to quadratic constraints. In this paper we show that a certain cl... |
Title: Using ASP with recent extensions for causal explanations |
Abstract: We examine the practicality for a user of using Answer Set Programming (ASP) for representing logical formalisms. We choose as an example a formalism aiming at capturing causal explanations from causal information. We provide an implementation, showing the naturalness and relative efficiency of this translati... |
Title: Automated Query Learning with Wikipedia and Genetic Programming |
Abstract: Most of the existing information retrieval systems are based on bag of words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the d... |
Title: Generalized Species Sampling Priors with Latent Beta reinforcements |
Abstract: Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of non-exchangeable species sampling sequences characterized by... |
Title: Efficient Optimization of Performance Measures by Classifier Adaptation |
Abstract: In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measur... |
Title: Split Bregman Method for Sparse Inverse Covariance Estimation with Matrix Iteration Acceleration |
Abstract: We consider the problem of estimating the inverse covariance matrix by maximizing the likelihood function with a penalty added to encourage the sparsity of the resulting matrix. We propose a new approach based on the split Bregman method to solve the regularized maximum likelihood estimation problem. We show ... |
Title: Local Consistency of Markov Chain Monte Carlo Methods |
Abstract: In this paper, we introduce the notion of efficiency (consistency) and examine some asymptotic properties of Markov chain Monte Carlo methods. We apply these results to the data augmentation (DA) procedure for independent and identically distributed observations. More precisely, we show that if both the sampl... |
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