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Title: Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction |
Abstract: We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because of the dual formula... |
Title: Approximate Bayesian Computation: a nonparametric perspective |
Abstract: Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well-suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics s_obs from the data and simulating summary statist... |
Title: Performing Nonlinear Blind Source Separation with Signal Invariants |
Abstract: Given a time series of multicomponent measurements x(t), the usual objective of nonlinear blind source separation (BSS) is to find a "source" time series s(t), comprised of statistically independent combinations of the measured components. In this paper, the source time series is required to have a density fu... |
Title: A Bayesian approach to the analysis of time symmetry in light curves: Reconsidering Scorpius X-1 occultations |
Abstract: We present a new approach to the analysis of time symmetry in light curves, such as those in the x-ray at the center of the Scorpius X-1 occultation debate. Our method uses a new parameterization for such events (the bilogistic event profile) and provides a clear, physically relevant characterization of each ... |
Title: Evolvability need not imply learnability |
Abstract: We show that Boolean functions expressible as monotone disjunctive normal forms are PAC-evolvable under a uniform distribution on the Boolean cube if the hypothesis size is allowed to remain fixed. We further show that this result is insufficient to prove the PAC-learnability of monotone Boolean functions, th... |
Title: Smooth Optimization Approach for Sparse Covariance Selection |
Abstract: In this paper we first study a smooth optimization approach for solving a class of nonsmooth strictly concave maximization problems whose objective functions admit smooth convex minimization reformulations. In particular, we apply Nesterov's smooth optimization technique [Y.E. Nesterov, Dokl. Akad. Nauk SSSR,... |
Title: Adaptive First-Order Methods for General Sparse Inverse Covariance Selection |
Abstract: In this paper, we consider estimating sparse inverse covariance of a Gaussian graphical model whose conditional independence is assumed to be partially known. Similarly as in [5], we formulate it as an $l_1$-norm penalized maximum likelihood estimation problem. Further, we propose an algorithm framework, and ... |
Title: Convex Optimization Methods for Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression |
Abstract: In this paper, we study convex optimization methods for computing the trace norm regularized least squares estimate in multivariate linear regression. The so-called factor estimation and selection (FES) method, recently proposed by Yuan et al. [22], conducts parameter estimation and factor selection simultane... |
Title: Optimal Tableau Decision Procedures for PDL |
Abstract: We reformulate Pratt's tableau decision procedure of checking satisfiability of a set of formulas in PDL. Our formulation is simpler and more direct for implementation. Extending the method we give the first EXPTIME (optimal) tableau decision procedure not based on transformation for checking consistency of a... |
Title: Induction of High-level Behaviors from Problem-solving Traces using Machine Learning Tools |
Abstract: This paper applies machine learning techniques to student modeling. It presents a method for discovering high-level student behaviors from a very large set of low-level traces corresponding to problem-solving actions in a learning environment. Basic actions are encoded into sets of domain-dependent attribute-... |
Title: Least-Squares Joint Diagonalization of a matrix set by a congruence transformation |
Abstract: The approximate joint diagonalization (AJD) is an important analytic tool at the base of numerous independent component analysis (ICA) and other blind source separation (BSS) methods, thus finding more and more applications in medical imaging analysis. In this work we present a new AJD algorithm named SDIAG (... |
Title: Stability Analysis and Learning Bounds for Transductive Regression Algorithms |
Abstract: This paper uses the notion of algorithmic stability to derive novel generalization bounds for several families of transductive regression algorithms, both by using convexity and closed-form solutions. Our analysis helps compare the stability of these algorithms. It also shows that a number of widely used tran... |
Title: Finding Exogenous Variables in Data with Many More Variables than Observations |
Abstract: Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations (p<n, p: the number of variables and n: the number of observations). However, modern datasets including gene expression data need high-dimensional causal modeling in challenging... |
Title: Empirical Likelihood Confidence Intervals for Nonparametric Functional Data Analysis |
Abstract: We consider the problem of constructing confidence intervals for nonparametric functional data analysis using empirical likelihood. In this doubly infinite-dimensional context, we demonstrate the Wilks's phenomenon and propose a bias-corrected construction that requires neither undersmoothing nor direct bias ... |
Title: Inference on Counterfactual Distributions |
Abstract: Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris p... |
Title: Color Dipole Moments for Edge Detection |
Abstract: Dipole and higher moments are physical quantities used to describe a charge distribution. In analogy with electromagnetism, it is possible to define the dipole moments for a gray-scale image, according to the single aspect of a gray-tone map. In this paper we define the color dipole moments for color images. ... |
Title: Bayesian MAP Model Selection of Chain Event Graphs |
Abstract: The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding asymmetric state spaces and the order in which events happe... |
Title: Dependency Pairs and Polynomial Path Orders |
Abstract: We show how polynomial path orders can be employed efficiently in conjunction with weak innermost dependency pairs to automatically certify polynomial runtime complexity of term rewrite systems and the polytime computability of the functions computed. The established techniques have been implemented and we pr... |
Title: Learning convex bodies is hard |
Abstract: We show that learning a convex body in $\RR^d$, given random samples from the body, requires $2^\Omega()$ samples. By learning a convex body we mean finding a set having at most $\eps$ relative symmetric difference with the input body. To prove the lower bound we construct a hard to learn family of convex bod... |
Title: An Investigation Report on Auction Mechanism Design |
Abstract: Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock o... |
Title: Language Diversity across the Consonant Inventories: A Study in the Framework of Complex Networks |
Abstract: n this paper, we attempt to explain the emergence of the linguistic diversity that exists across the consonant inventories of some of the major language families of the world through a complex network based growth model. There is only a single parameter for this model that is meant to introduce a small amount... |
Title: Generalized Rejection Sampling Schemes and Applications in Signal Processing |
Abstract: Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. However, in many problems of practical interest these techniques demand procedures for s... |
Title: Decomposition and Model Selection for Large Contingency Tables |
Abstract: Large contingency tables summarizing categorical variables arise in many areas. For example in biology when a large number of biomarkers are cross-tabulated according to their discrete expression level. Interactions of the variables are generally studied with log-linear models and the structure of a log-linea... |
Title: Online prediction of ovarian cancer |
Abstract: In this paper we apply computer learning methods to diagnosing ovarian cancer using the level of the standard biomarker CA125 in conjunction with information provided by mass-spectrometry. We are working with a new data set collected over a period of 7 years. Using the level of CA125 and mass-spectrometry pea... |
Title: On the closed-form solution of the rotation matrix arising in computer vision problems |
Abstract: We show the closed-form solution to the maximization of trace(A'R), where A is given and R is unknown rotation matrix. This problem occurs in many computer vision tasks involving optimal rotation matrix estimation. The solution has been continuously reinvented in different fields as part of specific problems.... |
Title: Fuzzy inference based mentality estimation for eye robot agent |
Abstract: Household robots need to communicate with human beings in a friendly fashion. To achieve better understanding of displayed information, an importance and a certainty of the information should be communicated together with the main information. The proposed intent expression system aims to convey this addition... |
Title: Intent expression using eye robot for mascot robot system |
Abstract: An intent expression system using eye robots is proposed for a mascot robot system from a viewpoint of humatronics. The eye robot aims at providing a basic interface method for an information terminal robot system. To achieve better understanding of the displayed information, the importance and the degree of ... |
Title: CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming |
Abstract: This papers develops a logical language for representing probabilistic causal laws. Our interest in such a language is twofold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level ... |
Title: Recovering the state sequence of hidden Markov models using mean-field approximations |
Abstract: Inferring the sequence of states from observations is one of the most fundamental problems in Hidden Markov Models. In statistical physics language, this problem is equivalent to computing the marginals of a one-dimensional model with a random external field. While this task can be accomplished through transf... |
Title: On Fodor on Darwin on Evolution |
Abstract: Jerry Fodor argues that Darwin was wrong about "natural selection" because (1) it is only a tautology rather than a scientific law that can support counterfactuals ("If X had happened, Y would have happened") and because (2) only minds can select. Hence Darwin's analogy with "artificial selection" by animal b... |
Title: Average Entropy Functions |
Abstract: The closure of the set of entropy functions associated with n discrete variables, Gammar*n, is a convex cone in (2n-1)- dimensional space, but its full characterization remains an open problem. In this paper, we map Gammar*n to an n-dimensional region Phi*n by averaging the joint entropies with the same numbe... |
Title: KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis |
Abstract: Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a dat... |
Title: Average and Quantile Effects in Nonseparable Panel Models |
Abstract: Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under time homogeneity conditions that are like "time is randomly assigned" or "time is an instrument." Partial identification re... |
Title: Boosting through Optimization of Margin Distributions |
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