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Abstract: This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced gra... |
Title: Approximate Lesion Localization in Dermoscopy Images |
Abstract: Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first s... |
Title: A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict |
Abstract: The focus of this paper is an approach to the modeling of longitudinal social network or relational data. Such data arise from measurements on pairs of objects or actors made at regular temporal intervals, resulting in a social network for each point in time. In this article we represent the network and tempo... |
Title: Optimal designs for rational function regression |
Abstract: We consider optimal non-sequential designs for a large class of (linear and nonlinear) regression models involving polynomials and rational functions with heteroscedastic noise also given by a polynomial or rational weight function. The proposed method treats D-, E-, A-, and $\Phi_p$-optimal designs in a unif... |
Title: Comparing Prediction Market Structures, With an Application to Market Making |
Abstract: Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Various market making algorithms have been proposed in the literature and deployed in practice, but there has been little effort to evaluate their benefits and disadvantages in a systematic manner. We introduce a n... |
Title: A comparison of score-based methods for estimating Bayesian networks using the Kullback-Leibler divergence |
Abstract: In this paper, we compare the performance of two methods for estimating Bayesian networks from data containing exogenous variables and random effects. The first method is fully Bayesian in which a prior distribution is placed on the exogenous variables, whereas the second method, which we call the residual ap... |
Title: Probabilistic Models over Ordered Partitions with Application in Learning to Rank |
Abstract: This paper addresses the general problem of modelling and learning rank data with ties. We propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering amon... |
Title: Is there a physically universal cellular automaton or Hamiltonian? |
Abstract: It is known that both quantum and classical cellular automata (CA) exist that are computationally universal in the sense that they can simulate, after appropriate initialization, any quantum or classical computation, respectively. Here we introduce a different notion of universality: a CA is called physically... |
Title: Computing Optimal Experimental Designs via Interior Point Method |
Abstract: In this paper, we study optimal experimental design problems with a broad class of smooth convex optimality criteria, including the classical A-, D- and p th mean criterion. In particular, we propose an interior point (IP) method for them and establish its global convergence. Furthermore, by exploiting the st... |
Title: A Hierarchical Bayesian Framework for Constructing Sparsity-inducing Priors |
Abstract: Variable selection techniques have become increasingly popular amongst statisticians due to an increased number of regression and classification applications involving high-dimensional data where we expect some predictors to be unimportant. In this context, Bayesian variable selection techniques involving Mar... |
Title: Robust Bayesian variable selection with sub-harmonic priors |
Abstract: This paper studies Bayesian variable selection in linear models with general spherically symmetric error distributions. We propose sub-harmonic priors which arise as a class of mixtures of Zellner's g-priors for which the Bayes factors are independent of the underlying error distribution, as long as it is in ... |
Title: Evolutionary Computational Method of Facial Expression Analysis for Content-based Video Retrieval using 2-Dimensional Cellular Automata |
Abstract: In this paper, Deterministic Cellular Automata (DCA) based video shot classification and retrieval is proposed. The deterministic 2D Cellular automata model captures the human facial expressions, both spontaneous and posed. The determinism stems from the fact that the facial muscle actions are standardized by... |
Title: Complexity of Non-Monotonic Logics |
Abstract: Over the past few decades, non-monotonic reasoning has developed to be one of the most important topics in computational logic and artificial intelligence. Different ways to introduce non-monotonic aspects to classical logic have been considered, e.g., extension with default rules, extension with modal belief... |
Title: AI 3D Cybug Gaming |
Abstract: In this short paper I briefly discuss 3D war Game based on artificial intelligence concepts called AI WAR. Going in to the details, I present the importance of CAICL language and how this language is used in AI WAR. Moreover I also present a designed and implemented 3D War Cybug for AI WAR using CAICL and dis... |
Title: Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data |
Abstract: Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial ti... |
Title: The Complexity of Causality and Responsibility for Query Answers and non-Answers |
Abstract: An answer to a query has a well-defined lineage expression (alternatively called how-provenance) that explains how the answer was derived. Recent work has also shown how to compute the lineage of a non-answer to a query. However, the cause of an answer or non-answer is a more subtle notion and consists, in ge... |
Title: Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss |
Abstract: Sequential estimation of the success probability $p$ in inverse binomial sampling is considered in this paper. For any estimator $\hat p$, its quality is measured by the risk associated with normalized loss functions of linear-linear or inverse-linear form. These functions are possibly asymmetric, with arbitr... |
Title: Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples |
Abstract: The two-parameter Birnbaum-Saunders distribution has been used succesfully to model fatigue failure times. Although censoring is typical in reliability and survival studies, little work has been published on the analysis of censored data for this distribution. In this paper, we address the issue of performing... |
Title: Multi-Agent Only-Knowing Revisited |
Abstract: Levesque introduced the notion of only-knowing to precisely capture the beliefs of a knowledge base. He also showed how only-knowing can be used to formalize non-monotonic behavior within a monotonic logic. Despite its appeal, all attempts to extend only-knowing to the many agent case have undesirable propert... |
Title: Multiplex Structures: Patterns of Complexity in Real-World Networks |
Abstract: Complex network theory aims to model and analyze complex systems that consist of multiple and interdependent components. Among all studies on complex networks, topological structure analysis is of the most fundamental importance, as it represents a natural route to understand the dynamics, as well as to synth... |
Title: Ontology Temporal Evolution for Multi-Entity Bayesian Networks under Exogenous and Endogenous Semantic Updating |
Abstract: It is a challenge for any Knowledge Base reasoning to manage ubiquitous uncertain ontology as well as uncertain updating times, while achieving acceptable service levels at minimum computational cost. This paper proposes an application-independent merging ontologies for any open interaction system. A solution... |
Title: Proximal Methods for Hierarchical Sparse Coding |
Abstract: Sparse coding consists in representing signals as sparse linear combinations of atoms selected from a dictionary. We consider an extension of this framework where the atoms are further assumed to be embedded in a tree. This is achieved using a recently introduced tree-structured sparse regularization norm, wh... |
Title: Accurate inference for a one parameter distribution based on the mean of a transformed sample |
Abstract: A great deal of inference in statistics is based on making the approximation that a statistic is normally distributed. The error in doing so is generally $O(n^-1/2)$ and can be very considerable when the distribution is heavily biased or skew. This note shows how one may reduce this error to $O(n^-(j+1)/2)$, ... |
Title: Computing the confidence levels for a root-mean-square test of goodness-of-fit, II |
Abstract: This paper extends our earlier article, "Computing the confidence levels for a root-mean-square test of goodness-of-fit;" unlike in the earlier article, the models in the present paper involve parameter estimation -- both the null and alternative hypotheses in the associated tests are composite. We provide ef... |
Title: PhishDef: URL Names Say It All |
Abstract: Phishing is an increasingly sophisticated method to steal personal user information using sites that pretend to be legitimate. In this paper, we take the following steps to identify phishing URLs. First, we carefully select lexical features of the URLs that are resistant to obfuscation techniques used by atta... |
Title: Bayesian Adaptive Lasso |
Abstract: We propose the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient estimation in linear regression. The BaLasso is adaptive to the signal level by adopting different shrinkage for different coefficients. Furthermore, we provide a model selection machinery for the BaLasso by assessing the ... |
Title: The Predictive Lasso |
Abstract: We propose a shrinkage procedure for simultaneous variable selection and estimation in generalized linear models (GLMs) with an explicit predictive motivation. The procedure estimates the coefficients by minimizing the Kullback-Leibler divergence of a set of predictive distributions to the corresponding predi... |
Title: Estimation of distribution functions in measurement error models |
Abstract: Many practical problems are related to the pointwise estimation of dis- tribution functions when data contains measurement errors. Motivation for these problems comes from diverse fields such as astronomy, reliability, quality control, public health and survey data. Recently, Dattner, Goldenshluger and Judits... |
Title: Reinforcement Learning by Comparing Immediate Reward |
Abstract: This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate rewards using a variation of Q-Learning algorithm. Unlike the conventional Q-Learning, the proposed algorithm compares current reward with immediate reward of past move and work accordingly. Relative reward base... |
Title: Efficient Bayesian Community Detection using Non-negative Matrix Factorisation |
Abstract: Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilises the Bayesian non-negative matrix factorisation (NMF) model to produce a probabilistic output for node memberships. The scheme has the advantage of computational ... |
Title: Approximate variances for tapered spectral estimates |
Abstract: We propose an approximation of the asymptotic variance that removes a certain discontinuity in the usual formula for the raw and the smoothed periodogram in case a data taper is used. It is based on an approximation of the covariance of the (tapered) periodogram at two arbitrary frequencies. Exact computation... |
Title: Minimization Strategies for Maximally Parallel Multiset Rewriting Systems |
Abstract: Maximally parallel multiset rewriting systems (MPMRS) give a convenient way to express relations between unstructured objects. The functioning of various computational devices may be expressed in terms of MPMRS (e.g., register machines and many variants of P systems). In particular, this means that MPMRS are ... |
Title: Pac-bayesian bounds for sparse regression estimation with exponential weights |
Abstract: We consider the sparse regression model where the number of parameters $p$ is larger than the sample size $n$. The difficulty when considering high-dimensional problems is to propose estimators achieving a good compromise between statistical and computational performances. The BIC estimator for instance perfo... |
Title: Calibrated Surrogate Losses for Classification with Label-Dependent Costs |
Abstract: We present surrogate regret bounds for arbitrary surrogate losses in the context of binary classification with label-dependent costs. Such bounds relate a classifier's risk, assessed with respect to a surrogate loss, to its cost-sensitive classification risk. Two approaches to surrogate regret bounds are deve... |
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