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Title: Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm |
Abstract: We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly. We introduce a weighted version of the trace-norm regularizer that works well also with non-uniform sampling. Our experimental results demonstrate that the weighted tra... |
Title: Model-Driven Constraint Programming |
Abstract: Constraint programming can definitely be seen as a model-driven paradigm. The users write programs for modeling problems. These programs are mapped to executable models to calculate the solutions. This paper focuses on efficient model management (definition and transformation). From this point of view, we pro... |
Title: Reconstruction of signals with unknown spectra in information field theory with parameter uncertainty |
Abstract: The optimal reconstruction of cosmic metric perturbations and other signals requires knowledge of their power spectra and other parameters. If these are not known a priori, they have to be measured simultaneously from the same data used for the signal reconstruction. We formulate the general problem of signal... |
Title: Geometric approach to sampling and communication |
Abstract: Relationships that exist between the classical, Shannon-type, and geometric-based approaches to sampling are investigated. Some aspects of coding and communication through a Gaussian channel are considered. In particular, a constructive method to determine the quantizing dimension in Zador's theorem is provid... |
Title: Rewriting Constraint Models with Metamodels |
Abstract: An important challenge in constraint programming is to rewrite constraint models into executable programs calculat- ing the solutions. This phase of constraint processing may require translations between constraint programming lan- guages, transformations of constraint representations, model optimizations, an... |
Title: Using ATL to define advanced and flexible constraint model transformations |
Abstract: Transforming constraint models is an important task in re- cent constraint programming systems. User-understandable models are defined during the modeling phase but rewriting or tuning them is manda- tory to get solving-efficient models. We propose a new architecture al- lowing to define bridges between any (... |
Title: Convergence of Bayesian Control Rule |
Abstract: Recently, new approaches to adaptive control have sought to reformulate the problem as a minimization of a relative entropy criterion to obtain tractable solutions. In particular, it has been shown that minimizing the expected deviation from the causal input-output dependencies of the true plant leads to a ne... |
Title: Structured, sparse regression with application to HIV drug resistance |
Abstract: We introduce a new version of forward stepwise regression. Our modification finds solutions to regression problems where the selected predictors appear in a structured pattern, with respect to a predefined distance measure over the candidate predictors. Our method is motivated by the problem of predicting HIV... |
Title: A new approach to content-based file type detection |
Abstract: File type identification and file type clustering may be difficult tasks that have an increasingly importance in the field of computer and network security. Classical methods of file type detection including considering file extensions and magic bytes can be easily spoofed. Content-based file type detection i... |
Title: A Complete Characterization of Statistical Query Learning with Applications to Evolvability |
Abstract: Statistical query (SQ) learning model of Kearns (1993) is a natural restriction of the PAC learning model in which a learning algorithm is allowed to obtain estimates of statistical properties of the examples but cannot see the examples themselves. We describe a new and simple characterization of the query co... |
Title: Efficiently Discovering Hammock Paths from Induced Similarity Networks |
Abstract: Similarity networks are important abstractions in many information management applications such as recommender systems, corpora analysis, and medical informatics. For instance, by inducing similarity networks between movies rated similarly by users, or between documents containing common terms, and or between... |
Title: Message-Passing Algorithms: Reparameterizations and Splittings |
Abstract: The max-product algorithm, a local message-passing scheme that attempts to compute the most probable assignment (MAP) of a given probability distribution, has been successfully employed as a method of approximate inference for applications arising in coding theory, computer vision, and machine learning. Howev... |
Title: Coarse-grained modeling of multiscale diffusions: the p-variation estimates |
Abstract: We study the problem of estimating parameters of the limiting equation of a multiscale diffusion in the case of averaging and homogenization, given data from the corresponding multiscale system. First, we review some recent results that make use of the maximum likelihood of the limiting equation. In particula... |
Title: Asymptotically Stable Walking of a Five-Link Underactuated 3D Bipedal Robot |
Abstract: This paper presents three feedback controllers that achieve an asymptotically stable, periodic, and fast walking gait for a 3D (spatial) bipedal robot consisting of a torso, two legs, and passive (unactuated) point feet. The contact between the robot and the walking surface is assumed to inhibit yaw rotation.... |
Title: Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation |
Abstract: We propose a new approach to the analysis of Loopy Belief Propagation (LBP) by establishing a formula that connects the Hessian of the Bethe free energy with the edge zeta function. The formula has a number of theoretical implications on LBP. It is applied to give a sufficient condition that the Hessian of th... |
Title: High-dimensional variable selection for Cox's proportional hazards model |
Abstract: Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technology advance has made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technolog... |
Title: Co-channel Interference Cancellation for Space-Time Coded OFDM Systems Using Adaptive Beamforming and Null Deepening |
Abstract: Combined with space-time coding, the orthogonal frequency division multiplexing (OFDM) system explores space diversity. It is a potential scheme to offer spectral efficiency and robust high data rate transmissions over frequency-selective fading channel. However, space-time coding impairs the system ability t... |
Title: Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection |
Abstract: The SSIM-optimized exact global histogram specification (EGHS) is shown to converge in the sense that the first order approximation of the result's quality (i.e., its structural similarity with input) does not decrease in an iteration, when the step size is small. Each iteration is composed of SSIM gradient a... |
Title: Interactive Submodular Set Cover |
Abstract: We introduce a natural generalization of submodular set cover and exact active learning with a finite hypothesis class (query learning). We call this new problem interactive submodular set cover. Applications include advertising in social networks with hidden information. We give an approximation guarantee fo... |
Title: Approximation by log-concave distributions, with applications to regression |
Abstract: We study the approximation of arbitrary distributions $P$ on $d$-dimensional space by distributions with log-concave density. Approximation means minimizing a Kullback--Leibler-type functional. We show that such an approximation exists if and only if $P$ has finite first moments and is not supported by some h... |
Title: Asymptotic risks of Viterbi segmentation |
Abstract: We consider the maximum likelihood (Viterbi) alignment of a hidden Markov model (HMM). In an HMM, the underlying Markov chain is usually hidden and the Viterbi alignment is often used as the estimate of it. This approach will be referred to as the Viterbi segmentation. The goodness of the Viterbi segmentation... |
Title: Improved EM for Mixture Proportions with Applications to Nonparametric ML Estimation for Censored Data |
Abstract: Improved EM strategies, based on the idea of efficient data augmentation (Meng and van Dyk 1997, 1998), are presented for ML estimation of mixture proportions. The resulting algorithms inherit the simplicity, ease of implementation, and monotonic convergence properties of EM, but have considerably improved sp... |
Title: Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast with Algebraic Optimal Step Size |
Abstract: Independent component analysis (ICA) aims at decomposing an observed random vector into statistically independent variables. Deflation-based implementations, such as the popular one-unit FastICA algorithm and its variants, extract the independent components one after another. A novel method for deflationary I... |
Title: Plugin procedure in segmentation and application to hyperspectral image segmentation |
Abstract: In this article we give our contribution to the problem of segmentation with plug-in procedures. We give general sufficient conditions under which plug in procedure are efficient. We also give an algorithm that satisfy these conditions. We give an application of the used algorithm to hyperspectral images segm... |
Title: Estimation for High-Dimensional Linear Mixed-Effects Models Using $\ell_1$-Penalization |
Abstract: We propose an $\ell_1$-penalized estimation procedure for high-dimensional linear mixed-effects models. The models are useful whenever there is a grouping structure among high-dimensional observations, i.e. for clustered data. We prove a consistency and an oracle optimality result and we develop an algorithm ... |
Title: Non-equilibrium dynamics of stochastic point processes with refractoriness |
Abstract: Stochastic point processes with refractoriness appear frequently in the quantitative analysis of physical and biological systems, such as the generation of action potentials by nerve cells, the release and reuptake of vesicles at a synapse, and the counting of particles by detector devices. Here we present an... |
Title: Partition Decoupling for Multi-gene Analysis of Gene Expression Profiling Data |
Abstract: We present the extention and application of a new unsupervised statistical learning technique--the Partition Decoupling Method--to gene expression data. Because it has the ability to reveal non-linear and non-convex geometries present in the data, the PDM is an improvement over typical gene expression analysi... |
Title: Supervised Learning of Digital image restoration based on Quantization Nearest Neighbor algorithm |
Abstract: In this paper, an algorithm is proposed for Image Restoration. Such algorithm is different from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operators are utilized for designing the Quant... |
Title: Word level Script Identification from Bangla and Devanagri Handwritten Texts mixed with Roman Script |
Abstract: India is a multi-lingual country where Roman script is often used alongside different Indic scripts in a text document. To develop a script specific handwritten Optical Character Recognition (OCR) system, it is therefore necessary to identify the scripts of handwritten text correctly. In this paper, we presen... |
Title: A fuzzified BRAIN algorithm for learning DNF from incomplete data |
Abstract: Aim of this paper is to address the problem of learning Boolean functions from training data with missing values. We present an extension of the BRAIN algorithm, called U-BRAIN (Uncertainty-managing Batch Relevance-based Artificial INtelligence), conceived for learning DNF Boolean formulas from partial truth ... |
Title: Query Learning with Exponential Query Costs |
Abstract: In query learning, the goal is to identify an unknown object while minimizing the number of "yes" or "no" questions (queries) posed about that object. A well-studied algorithm for query learning is known as generalized binary search (GBS). We show that GBS is a greedy algorithm to optimize the expected number... |
Title: Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier |
Abstract: A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of... |
Title: Supervised Classification Performance of Multispectral Images |
Abstract: Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most ... |
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