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Abstract: In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-singleoutput systems. The proposed method is an actor-critic syst... |
Title: A New Sufficient Condition for 1-Coverage to Imply Connectivity |
Abstract: An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully the active nodes must maintain both sensing coverage and network conn... |
Title: Point process modeling for directed interaction networks |
Abstract: Network data often take the form of repeated interactions between senders and receivers tabulated over time. A primary question to ask of such data is which traits and behaviors are predictive of interaction. To answer this question, a model is introduced for treating directed interactions as a multivariate p... |
Title: Least Squares Ranking on Graphs |
Abstract: Given a set of alternatives to be ranked, and some pairwise comparison data, ranking is a least squares computation on a graph. The vertices are the alternatives, and the edge values comprise the comparison data. The basic idea is very simple and old: come up with values on vertices such that their difference... |
Title: Online Expectation-Maximisation |
Abstract: Tutorial chapter on the Online EM algorithm to appear in the volume 'Mixtures' edited by Kerrie Mengersen, Mike Titterington and Christian P. Robert. |
Title: Efficient Bayesian Inference for Generalized Bradley-Terry Models |
Abstract: The Bradley-Terry model is a popular approach to describe probabilities of the possible outcomes when elements of a set are repeatedly compared with one another in pairs. It has found many applications including animal behaviour, chess ranking and multiclass classification. Numerous extensions of the basic mo... |
Title: Fundamentals of Mathematical Theory of Emotional Robots |
Abstract: In this book we introduce a mathematically formalized concept of emotion, robot's education and other psychological parameters of intelligent robots. We also introduce unitless coefficients characterizing an emotional memory of a robot. Besides, the effect of a robot's memory upon its emotional behavior is st... |
Title: Statistical mechanics of digital halftoning |
Abstract: We consider the problem of digital halftoning from the view point of statistical mechanics. The digital halftoning is a sort of image processing, namely, representing each grayscale in terms of black and white binary dots. The digital halftoning is achieved by making use of the threshold mask, namely, for eac... |
Title: Blackwell Approachability and Low-Regret Learning are Equivalent |
Abstract: We consider the celebrated Blackwell Approachability Theorem for two-player games with vector payoffs. We show that Blackwell's result is equivalent, via efficient reductions, to the existence of "no-regret" algorithms for Online Linear Optimization. Indeed, we show that any algorithm for one such problem can... |
Title: A Separable Model for Dynamic Networks |
Abstract: Models of dynamic networks --- networks that evolve over time --- have manifold applications. We develop a discrete-time generative model for social network evolution that inherits the richness and flexibility of the class of exponential-family random graph models. The model --- a Separable Temporal ERGM (STE... |
Title: Discrete Partitioning and Coverage Control for Gossiping Robots |
Abstract: We propose distributed algorithms to automatically deploy a team of mobile robots to partition and provide coverage of a non-convex environment. To handle arbitrary non-convex environments, we represent them as graphs. Our partitioning and coverage algorithm requires only short-range, unreliable pairwise "gos... |
Title: Using Model-based Overlapping Seed Expansion to detect highly overlapping community structure |
Abstract: As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of assigning each node to more than a single community. The performance of these algorithms... |
Title: Metropolising forward particle filtering backward sampling and Rao-Blackwellisation of Metropolised particle smoothers |
Abstract: Smoothing in state-space models amounts to computing the conditional distribution of the latent state trajectory, given observations, or expectations of functionals of the state trajectory with respect to this distributions. For models that are not linear Gaussian or possess finite state space, smoothing dist... |
Title: Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey |
Abstract: We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the... |
Title: Strong rules for discarding predictors in lasso-type problems |
Abstract: We consider rules for discarding predictors in lasso regression and related problems, for computational efficiency. El Ghaoui et al (2010) propose "SAFE" rules that guarantee that a coefficient will be zero in the solution, based on the inner products of each predictor with the outcome. In this paper we propo... |
Title: Single Frame Image super Resolution using Learned Directionlets |
Abstract: In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions... |
Title: Image Segmentation with Multidimensional Refinement Indicators |
Abstract: We transpose an optimal control technique to the image segmentation problem. The idea is to consider image segmentation as a parameter estimation problem. The parameter to estimate is the color of the pixels of the image. We use the adaptive parameterization technique which builds iteratively an optimal repre... |
Title: Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering |
Abstract: In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to tra... |
Title: Free energy computations by minimization of Kullback-Leibler divergence: an efficient adaptive biasing potential method for sparse representations |
Abstract: The present paper proposes an adaptive biasing potential for the computation of free energy landscapes. It is motivated by statistical learning arguments and unifies the tasks of biasing the molecular dynamics to escape free energy wells and estimating the free energy function, under the same objective. It of... |
Title: Efficient Bayesian Inference for Switching State-Space Models using Discrete Particle Markov Chain Monte Carlo Methods |
Abstract: Switching state-space models (SSSM) are a very popular class of time series models that have found many applications in statistics, econometrics and advanced signal processing. Bayesian inference for these models typically relies on Markov chain Monte Carlo (MCMC) techniques. However, even sophisticated MCMC ... |
Title: Extended Active Learning Method |
Abstract: Active Learning Method (ALM) is a soft computing method which is used for modeling and control, based on fuzzy logic. Although ALM has shown that it acts well in dynamic environments, its operators cannot support it very well in complex situations due to losing data. Thus ALM can find better membership functi... |
Title: Modeling Non-Stationary Processes Through Dimension Expansion |
Abstract: In this paper, we propose a novel approach to modeling nonstationary spatial fields. The proposed method works by expanding the geographic plane over which these processes evolve into higher dimensional spaces, transforming and clarifying complex patterns in the physical plane. By combining aspects of multi-d... |
Title: Complex sequencing rules of birdsong can be explained by simple hidden Markov processes |
Abstract: Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors, it is crucial to characterize the statistical properties of the sequencing rules ... |
Title: Clustering using Unsupervised Binary Trees: CUBT |
Abstract: We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), considerat... |
Title: A hierarchical Bayesian approach to record linkage and population size problems |
Abstract: We propose and illustrate a hierarchical Bayesian approach for matching statistical records observed on different occasions. We show how this model can be profitably adopted both in record linkage problems and in capture--recapture setups, where the size of a finite population is the real object of interest. ... |
Title: Stability of Density-Based Clustering |
Abstract: High density clusters can be characterized by the connected components of a level set $L(\lambda) = \x:\ p(x)>\lambda\$ of the underlying probability density function $p$ generating the data, at some appropriate level $\lambda\geq 0$. The complete hierarchical clustering can be characterized by a cluster tree... |
Title: Reified unit resolution and the failed literal rule |
Abstract: Unit resolution can simplify a CNF formula or detect an inconsistency by repeatedly assign the variables occurring in unit clauses. Given any CNF formula sigma, we show that there exists a satisfiable CNF formula psi with size polynomially related to the size of sigma such that applying unit resolution to psi... |
Title: Emoticonsciousness |
Abstract: A temporal analysis of emoticon use in Swedish, Italian, German and English asynchronous electronic communication is reported. Emoticons are classified as positive, negative and neutral. Postings to newsgroups over a 66 week period are considered. The aggregate analysis of emoticon use in newsgroups for scien... |
Title: Simulation-based Bayesian analysis for multiple changepoints |
Abstract: This paper presents a Markov chain Monte Carlo method to generate approximate posterior samples in retrospective multiple changepoint problems where the number of changes is not known in advance. The method uses conjugate models whereby the marginal likelihood for the data between consecutive changepoints is ... |
Title: Block clustering with collapsed latent block models |
Abstract: We introduce a Bayesian extension of the latent block model for model-based block clustering of data matrices. Our approach considers a block model where block parameters may be integrated out. The result is a posterior defined over the number of clusters in rows and columns and cluster memberships. The numbe... |
Title: Balanced Reduction of Nonlinear Control Systems in Reproducing Kernel Hilbert Space |
Abstract: We introduce a novel data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction. The method rests on the assumption that the nonlinear system behaves linearly when lifted into a high (or infinite) dimensional featur... |
Title: Bounded Multivariate Surfaces On Monovariate Internal Functions |
Abstract: Combining the properties of monovariate internal functions as proposed in Kolmogorov superimposition theorem, in tandem with the bounds wielded by the multivariate formulation of Chebyshev inequality, a hybrid model is presented, that decomposes images into homogeneous probabilistically bounded multivariate s... |
Title: Classification with Scattering Operators |
Abstract: A scattering vector is a local descriptor including multiscale and multi-direction co-occurrence information. It is computed with a cascade of wavelet decompositions and complex modulus. This scattering representation is locally translation invariant and linearizes deformations. A supervised classification al... |
Title: Regularization Strategies and Empirical Bayesian Learning for MKL |
Abstract: Multiple kernel learning (MKL), structured sparsity, and multi-task learning have recently received considerable attention. In this paper, we show how different MKL algorithms can be understood as applications of either regularization on the kernel weights or block-norm-based regularization, which is more com... |
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