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Title: Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards |
Abstract: In the classic multi-armed bandits problem, the goal is to have a policy for dynamically operating arms that each yield stochastic rewards with unknown means. The key metric of interest is regret, defined as the gap between the expected total reward accumulated by an omniscient player that knows the reward me... |
Title: The Non-Bayesian Restless Multi-Armed Bandit: a Case of Near-Logarithmic Regret |
Abstract: In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are $N$ arms, with rewards on all arms evolving at each time as Markov chains with known parameters. A player seeks to activate $K \geq 1$ arms at each time in order to maximize the expected total reward obtained over multiple plays. RM... |
Title: Closed-Form Solutions to A Category of Nuclear Norm Minimization Problems |
Abstract: It is an efficient and effective strategy to utilize the nuclear norm approximation to learn low-rank matrices, which arise frequently in machine learning and computer vision. So the exploration of nuclear norm minimization problems is gaining much attention recently. In this paper we shall prove that the fol... |
Title: Variational approximation for heteroscedastic linear models and matching pursuit algorithms |
Abstract: Modern statistical applications involving large data sets have focused attention on statistical methodologies which are both efficient computationally and able to deal with the screening of large numbers of different candidate models. Here we consider computationally efficient variational Bayes approaches to ... |
Title: A Logical Charaterisation of Ordered Disjunction |
Abstract: In this paper we consider a logical treatment for the ordered disjunction operator 'x' introduced by Brewka, Niemel\"a and Syrj\"anen in their Logic Programs with Ordered Disjunctions (LPOD). LPODs are used to represent preferences in logic programming under the answer set semantics. Their semantics is define... |
Title: Fast Bivariate Penalized Splines: the Sandwich Smoother |
Abstract: We propose a fast penalized spline method for bivariate smoothing. Univariate P-spline smoothers (Eilers and Marx, 1996) are applied simultaneously along both coordinates. The new smoother has a sandwich form which suggested the name "sandwich smoother" to a referee. The sandwich smoother has a tensor product... |
Title: Learning in A Changing World: Restless Multi-Armed Bandit with Unknown Dynamics |
Abstract: We consider the restless multi-armed bandit (RMAB) problem with unknown dynamics in which a player chooses M out of N arms to play at each time. The reward state of each arm transits according to an unknown Markovian rule when it is played and evolves according to an arbitrary unknown random process when it i... |
Title: Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments |
Abstract: This paper proposes approaches for the analysis of multiple changepoint models when dependency in the data is modelled through a hierarchical Gaussian Markov random field. Integrated nested Laplace approximations are used to approximate data quantities, and an approximate filtering recursions approach is prop... |
Title: Tight Sample Complexity of Large-Margin Learning |
Abstract: We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L_2 regularization: We introduce the \gamma-adapted-dimension, which is a simple function of the spectrum of a distribution's covariance matrix, and show distribution-specific upper and lower ... |
Title: Application of a Quantum Ensemble Model to Linguistic Analysis |
Abstract: A new set of parameters to describe the word frequency behavior of texts is proposed. The analogy between the word frequency distribution and the Bose-distribution is suggested and the notion of "temperature" is introduced for this case. The calculations are made for English, Ukrainian, and the Guinean Manink... |
Title: Evolutionary distances in the twilight zone -- a rational kernel approach |
Abstract: Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string co... |
Title: Concentration inequalities of the cross-validation estimate for stable predictors |
Abstract: In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for stable predictors in the context of risk assessment. The notion of stability has been first introduced by and extended by , and to characterize class of predictors with infinite VC dimension... |
Title: Estimating Subagging by cross-validation |
Abstract: In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for subagged estimators, both for classification and regressor. General loss functions and class of predictors with both finite and infinite VC-dimension are considered. We slightly generalize t... |
Title: La r\'eduction de termes complexes dans les langues de sp\'ecialit\'e |
Abstract: Our study applies statistical methods to French and Italian corpora to examine the phenomenon of multi-word term reduction in specialty languages. There are two kinds of reduction: anaphoric and lexical. We show that anaphoric reduction depends on the discourse type (vulgarization, pedagogical, specialized) b... |
Title: Covered Clause Elimination |
Abstract: Generalizing the novel clause elimination procedures developed in [M. Heule, M. J\"arvisalo, and A. Biere. Clause elimination procedures for CNF formulas. In Proc. LPAR-17, volume 6397 of LNCS, pages 357-371. Springer, 2010.], we introduce explicit (CCE), hidden (HCCE), and asymmetric (ACCE) variants of a pro... |
Title: The semantic mapping of words and co-words in contexts |
Abstract: Meaning can be generated when information is related at a systemic level. Such a system can be an observer, but also a discourse, for example, operationalized as a set of documents. The measurement of semantics as similarity in patterns (correlations) and latent variables (factor analysis) has been enhanced b... |
Title: Fractal Geometry of Angular Momentum Evolution in Near-Keplerian Systems |
Abstract: In this paper, we propose a method to study the nature of resonant relaxation in near-Keplerian systems. Our technique is based on measuring the fractal dimension of the angular momentum trails and we use it to analyze the outcome of N-body simulations. With our method, we can reliably determine the timescale... |
Title: Classifying Clustering Schemes |
Abstract: Many clustering schemes are defined by optimizing an objective function defined on the partitions of the underlying set of a finite metric space. In this paper, we construct a framework for studying what happens when we instead impose various structural conditions on the clustering schemes, under the general ... |
Title: Bayesian Sequential Detection with Phase-Distributed Change Time and Nonlinear Penalty -- A POMDP Approach |
Abstract: We show that the optimal decision policy for several types of Bayesian sequential detection problems has a threshold switching curve structure on the space of posterior distributions. This is established by using lattice programming and stochastic orders in a partially observed Markov decision process (POMDP)... |
Title: Distributed Graph Coloring: An Approach Based on the Calling Behavior of Japanese Tree Frogs |
Abstract: Graph coloring, also known as vertex coloring, considers the problem of assigning colors to the nodes of a graph such that adjacent nodes do not share the same color. The optimization version of the problem concerns the minimization of the number of used colors. In this paper we deal with the problem of findi... |
Title: The Sample Complexity of Dictionary Learning |
Abstract: A large set of signals can sometimes be described sparsely using a dictionary, that is, every element can be represented as a linear combination of few elements from the dictionary. Algorithms for various signal processing applications, including classification, denoising and signal separation, learn a dictio... |
Title: Bayesian Modeling of a Human MMORPG Player |
Abstract: This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes a... |
Title: Seasonal fractional long-memory processes. A semiparametric estimation approach |
Abstract: This paper explores seasonal and long-memory time series properties by using the seasonal fractional ARIMA model when the seasonal data has one and two seasonal periods and short-memory counterparts. The stationarity and invertibility parameter conditions are established for the model studied. To estimate the... |
Title: On Theorem 2.3 in "Prediction, Learning, and Games" by Cesa-Bianchi and Lugosi |
Abstract: The note presents a modified proof of a loss bound for the exponentially weighted average forecaster with time-varying potential. The regret term of the algorithm is upper-bounded by sqrtn ln(N) (uniformly in n), where N is the number of experts and n is the number of steps. |
Title: Formulation Of A N-Degree Polynomial For Depth Estimation using a Single Image |
Abstract: The depth of a visible surface of a scene is the distance between the surface and the sensor. Recovering depth information from two-dimensional images of a scene is an important task in computer vision that can assist numerous applications such as object recognition, scene interpretation, obstacle avoidance, ... |
Title: Reinforcement Learning in Partially Observable Markov Decision Processes using Hybrid Probabilistic Logic Programs |
Abstract: We present a probabilistic logic programming framework to reinforcement learning, by integrating reinforce-ment learning, in POMDP environments, with normal hybrid probabilistic logic programs with probabilistic answer set seman-tics, that is capable of representing domain-specific knowledge. We formally prov... |
Title: Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces |
Abstract: The goal of this paper is the development of a novel approach for the problem of Noise Removal, based on the theory of Reproducing Kernels Hilbert Spaces (RKHS). The problem is cast as an optimization task in a RKHS, by taking advantage of the celebrated semiparametric Representer Theorem. Examples verify tha... |
Title: In All Likelihood, Deep Belief Is Not Enough |
Abstract: Statistical models of natural stimuli provide an important tool for researchers in the fields of machine learning and computational neuroscience. A canonical way to quantitatively assess and compare the performance of statistical models is given by the likelihood. One class of statistical models which has rec... |
Title: A ROAD to Classification in High Dimensional Space |
Abstract: For high-dimensional classification, it is well known that naively performing the Fisher discriminant rule leads to poor results due to diverging spectra and noise accumulation. Therefore, researchers proposed independence rules to circumvent the diverse spectra, and sparse independence rules to mitigate the ... |
Title: Network inference using asynchronously updated kinetic Ising Model |
Abstract: Network structures are reconstructed from dynamical data by respectively naive mean field (nMF) and Thouless-Anderson-Palmer (TAP) approximations. For TAP approximation, we use two methods to reconstruct the network: a) iteration method; b) casting the inference formula to a set of cubic equations and solving... |
Title: The Random Walk Metropolis: Linking Theory and Practice Through a Case Study |
Abstract: The random walk Metropolis (RWM) is one of the most common Markov chain Monte Carlo algorithms in practical use today. Its theoretical properties have been extensively explored for certain classes of target, and a number of results with important practical implications have been derived. This article draws to... |
Title: Multimodal Biometric Systems - Study to Improve Accuracy and Performance |
Abstract: Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Experimental studies show that Unimodal biometric systems had many disadvantages regarding per... |
Title: Classifying extremely imbalanced data sets |
Abstract: Imbalanced data sets containing much more background than signal instances are very common in particle physics, and will also be characteristic for the upcoming analyses of LHC data. Following up the work presented at ACAT 2008, we use the multivariate technique presented there (a rule growing algorithm with ... |
Title: Multiscale Inference of Matter Fields and Baryon Acoustic Oscillations from the Ly-alpha Forest |
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