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Abstract: In certain applications it is useful to fit multinomial distributions to observed data with a penalty term that encourages sparsity. For example, in probabilistic latent audio source decomposition one may wish to encode the assumption that only a few latent sources are active at any given time. The standard h... |
Title: Fast Reinforcement Learning for Energy-Efficient Wireless Communications |
Abstract: We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g. multimedia data) over a fading channel. Existing research on this topic utilizes either physical-layer centric solutions, namely power-control and adaptive modulation and coding (AMC), or system-level solutio... |
Title: Optimal learning rates for Kernel Conjugate Gradient regression |
Abstract: We prove rates of convergence in the statistical sense for kernel-based least squares regression using a conjugate gradient algorithm, where regularization against overfitting is obtained by early stopping. This method is directly related to Kernel Partial Least Squares, a regression method that combines supe... |
Title: The Attentive Perceptron |
Abstract: We propose a focus of attention mechanism to speed up the Perceptron algorithm. Focus of attention speeds up the Perceptron algorithm by lowering the number of features evaluated throughout training and prediction. Whereas the traditional Perceptron evaluates all the features of each example, the Attentive Pe... |
Title: Empirical Bayes methods corrected for small numbers of tests |
Abstract: Histogram-based empirical Bayes methods developed for analyzing data for large numbers of genes, SNPs, or other biological features tend to have large biases when applied to data with a smaller number of features such as genes with expression measured conventionally, proteins, and metabolites. To analyze such... |
Title: A Comprehensive Survey of Data Mining-based Fraud Detection Research |
Abstract: This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collecte... |
Title: Efficient Knowledge Base Management in DCSP |
Abstract: DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI (Artificial Intelligence). There are many application problems in distributed AI that can be formalized as DSCPs. With the increasing complexity and problem size of the application problems in AI, the required sto... |
Title: How to Extract the Geometry and Topology from Very Large 3D Segmentations |
Abstract: Segmentation is often an essential intermediate step in image analysis. A volume segmentation characterizes the underlying volume image in terms of geometric information--segments, faces between segments, curves in which several faces meet--as well as a topology on these objects. Existing algorithms encode th... |
Title: An Embarrassingly Simple Speed-Up of Belief Propagation with Robust Potentials |
Abstract: We present an exact method of greatly speeding up belief propagation (BP) for a wide variety of potential functions in pairwise MRFs and other graphical models. Specifically, our technique applies whenever the pairwise potentials have been \em truncated to a constant value for most pairs of states, as is comm... |
Title: Mantis: Predicting System Performance through Program Analysis and Modeling |
Abstract: We present Mantis, a new framework that automatically predicts program performance with high accuracy. Mantis integrates techniques from programming language and machine learning for performance modeling, and is a radical departure from traditional approaches. Mantis extracts program features, which are infor... |
Title: Online Learning in Opportunistic Spectrum Access: A Restless Bandit Approach |
Abstract: We consider an opportunistic spectrum access (OSA) problem where the time-varying condition of each channel (e.g., as a result of random fading or certain primary users' activities) is modeled as an arbitrary finite-state Markov chain. At each instance of time, a (secondary) user probes a channel and collects... |
Title: A model selection approach to genome wide association studies |
Abstract: For the vast majority of genome wide association studies (GWAS) published so far, statistical analysis was performed by testing markers individually. In this article we present some elementary statistical considerations which clearly show that in case of complex traits the approach based on multiple regressio... |
Title: Validated Intraclass Correlation Statistics to Test Item Performance Models |
Abstract: A new method, with an application program in Matlab code, is proposed for testing item performance models on empirical databases. This method uses data intraclass correlation statistics as expected correlations to which one compares simple functions of correlations between model predictions and observed item ... |
Title: Group-Lasso on Splines for Spectrum Cartography |
Abstract: The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based ap... |
Title: Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop MIMO Cooperative Systems |
Abstract: In this paper, we consider a queue-aware distributive resource control algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay buffering is an effective way to reduce the intrinsic half-duplex penalty in cooperative systems. The complex interactions of the queues at the source node and ... |
Title: Steepest Ascent Hill Climbing For A Mathematical Problem |
Abstract: The paper proposes artificial intelligence technique called hill climbing to find numerical solutions of Diophantine Equations. Such equations are important as they have many applications in fields like public key cryptography, integer factorization, algebraic curves, projective curves and data dependency in ... |
Title: Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation |
Abstract: Using a collection of simulated an real benchmarks, we compare Bayesian and frequentist regularization approaches under a low informative constraint when the number of variables is almost equal to the number of observations on simulated and real datasets. This comparison includes new global noninformative app... |
Title: A Microwave Imaging and Enhancement Technique from Noisy Synthetic Data |
Abstract: An inverse iterative algorithm for microwave imaging based on moment method solution is presented here. The iterative scheme has been developed on constrained optimization technique and is certain to converge. Different mesh size for the model has been used here to overcome the Inverse Crime. The synthetic da... |
Title: Likelihood Inference for Models with Unobservables: Another View |
Abstract: There have been controversies among statisticians on (i) what to model and (ii) how to make inferences from models with unobservables. One such controversy concerns the difference between estimation methods for the marginal means not necessarily having a probabilistic basis and statistical models having unobs... |
Title: Model Assessment Tools for a Model False World |
Abstract: A standard goal of model evaluation and selection is to find a model that approximates the truth well while at the same time is as parsimonious as possible. In this paper we emphasize the point of view that the models under consideration are almost always false, if viewed realistically, and so we should analy... |
Title: Inference and Modeling with Log-concave Distributions |
Abstract: Log-concave distributions are an attractive choice for modeling and inference, for several reasons: The class of log-concave distributions contains most of the commonly used parametric distributions and thus is a rich and flexible nonparametric class of distributions. Further, the MLE exists and can be comput... |
Title: Interval Estimation for Messy Observational Data |
Abstract: We review some aspects of Bayesian and frequentist interval estimation, focusing first on their relative strengths and weaknesses when used in "clean" or "textbook" contexts. We then turn attention to observational-data situations which are "messy," where modeling that acknowledges the limitations of study de... |
Title: The Impact of Levene's Test of Equality of Variances on Statistical Theory and Practice |
Abstract: In many applications, the underlying scientific question concerns whether the variances of $k$ samples are equal. There are a substantial number of tests for this problem. Many of them rely on the assumption of normality and are not robust to its violation. In 1960 Professor Howard Levene proposed a new appro... |
Title: A Conversation with Leo Goodman |
Abstract: Leo A. Goodman was born on August 7, 1928 in New York City. He received his A.B. degree, summa cum laude, in 1948 from Syracuse University, majoring in mathematics and sociology. He went on to pursue graduate studies in mathematics, with an emphasis on mathematical statistics, in the Mathematics Department at... |
Title: Visual-hint Boundary to Segment Algorithm for Image Segmentation |
Abstract: Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual int... |
Title: Convolutional Matching Pursuit and Dictionary Training |
Abstract: Matching pursuit and K-SVD is demonstrated in the translation invariant setting |
Title: Asymptotic Normality of Support Vector Machine Variants and Other Regularized Kernel Methods |
Abstract: In nonparametric classification and regression problems, regularized kernel methods, in particular support vector machines, attract much attention in theoretical and in applied statistics. In an abstract sense, regularized kernel methods (simply called SVMs here) can be seen as regularized M-estimators for a ... |
Title: Regularizers for Structured Sparsity |
Abstract: We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. This problem is relevant in machine learning, statistics and signal processing. It is well known that a linear regression can benefit from knowledge that the underlying regr... |
Title: Real-time Robust Principal Components' Pursuit |
Abstract: In the recent work of Candes et al, the problem of recovering low rank matrix corrupted by i.i.d. sparse outliers is studied and a very elegant solution, principal component pursuit, is proposed. It is motivated as a tool for video surveillance applications with the background image sequence forming the low r... |
Title: Local Optimality of User Choices and Collaborative Competitive Filtering |
Abstract: While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of use... |
Title: Statistical inference optimized with respect to the observed sample for single or multiple comparisons |
Abstract: The normalized maximum likelihood (NML) is a recent penalized likelihood that has properties that justify defining the amount of discrimination information (DI) in the data supporting an alternative hypothesis over a null hypothesis as the logarithm of an NML ratio, namely, the alternative hypothesis NML divi... |
Title: Implementing regularization implicitly via approximate eigenvector computation |
Abstract: Regularization is a powerful technique for extracting useful information from noisy data. Typically, it is implemented by adding some sort of norm constraint to an objective function and then exactly optimizing the modified objective function. This procedure often leads to optimization problems that are compu... |
Title: A bagging SVM to learn from positive and unlabeled examples |
Abstract: We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting. This problem, often referred to as , differs from the standard supervised classification problem by the lack of negative examples in the traini... |
Title: Estimation of low-rank tensors via convex optimization |
Abstract: In this paper, we propose three approaches for the estimation of the Tucker decomposition of multi-way arrays (tensors) from partial observations. All approaches are formulated as convex minimization problems. Therefore, the minimum is guaranteed to be unique. The proposed approaches can automatically estimat... |
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