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Title: Bayesian Nonparametric Variable Selection as an Exploratory Tool for Finding Genes that Matter |
Abstract: High-throughput scientific studies involving no clear a'priori hypothesis are common. For example, a large-scale genomic study of a disease may examine thousands of genes without hypothesizing that any specific gene is responsible for the disease. In these studies, the objective is to explore a large number o... |
Title: Predicting Positive and Negative Links in Online Social Networks |
Abstract: We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdo... |
Title: The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter |
Abstract: It has often been taken as a working assumption that directed links in information networks are frequently formed by "short-cutting" a two-step path between the source and the destination -- a kind of implicit "link copying" analogous to the process of triadic closure in social networks. Despite the role of t... |
Title: Structure-Aware Stochastic Control for Transmission Scheduling |
Abstract: In this paper, we consider the problem of real-time transmission scheduling over time-varying channels. We first formulate the transmission scheduling problem as a Markov decision process (MDP) and systematically unravel the structural properties (e.g. concavity in the state-value function and monotonicity in... |
Title: Inductive Logic Programming in Databases: from Datalog to DL+log |
Abstract: In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of KR aspects related to database... |
Title: Causality and Statistical Learning |
Abstract: We review some approaches and philosophies of causal inference coming from sociology, economics, computer science, cognitive science, and statistics |
Title: Release ZERO.0.1 of package RefereeToolbox |
Abstract: RefereeToolbox is a java package implementing combination operators for fusing evidences. It is downloadable from: http://refereefunction.fredericdambreville.com/releases RefereeToolbox is based on an interpretation of the fusion rules by means of Referee Functions. This approach implies a dissociation betwee... |
Title: The role of semantics in mining frequent patterns from knowledge bases in description logics with rules |
Abstract: We propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular we consider the setting of using a language that combines description logics with DL-safe rules. This setting is important for the practical application of data mining to the ... |
Title: Near-Optimal Evasion of Convex-Inducing Classifiers |
Abstract: Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms t... |
Title: Targeted Event Detection |
Abstract: We consider the problem of event detection based upon a (typically multivariate) data stream characterizing some system. Most of the time the system is quiescent - nothing of interest is happening - but occasionally events of interest occur. The goal of event detection is to raise an alarm as soon as possible... |
Title: A note on the Berman condition |
Abstract: It is established that if a time series satisfies the Berman condition, and another related (summability) condition, the result of filtering that series through a certain type of filter also satisfies the two conditions. In particular it follows that if $X_t$ satisfies the two conditions and if $X_t$ and $a_t... |
Title: Universal Regularizers For Robust Sparse Coding and Modeling |
Abstract: Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choi... |
Title: Agreement Maintenance Based on Schema and Ontology Change in P2P Environment |
Abstract: This paper is concern about developing a semantic agreement maintenance method based on semantic distance by calculating the change of local schema or ontology. This approach is important in dynamic and autonomous environment, in which the current approach assumed that agreement or mapping in static environme... |
Title: Covariance-Adaptive Slice Sampling |
Abstract: We describe two slice sampling methods for taking multivariate steps using the crumb framework. These methods use the gradients at rejected proposals to adapt to the local curvature of the log-density surface, a technique that can produce much better proposals when parameters are highly correlated. We evaluat... |
Title: Probability distributions with summary graph structure |
Abstract: A set of independence statements may define the independence structure of interest in a family of joint probability distributions. This structure is often captured by a graph that consists of nodes representing the random variables and of edges that couple node pairs. One important class contains regression g... |
Title: Pattern recognition using inverse resonance filtration |
Abstract: An approach to textures pattern recognition based on inverse resonance filtration (IRF) is considered. A set of principal resonance harmonics of textured image signal fluctuations eigen harmonic decomposition (EHD) is used for the IRF design. It was shown that EHD is invariant to textured image linear shift. ... |
Title: Graphics Processing Units and High-Dimensional Optimization |
Abstract: This paper discusses the potential of graphics processing units (GPUs) in high-dimensional optimization problems. A single GPU card with hundreds of arithmetic cores can be inserted in a personal computer and dramatically accelerates many statistical algorithms. To exploit these devices fully, optimization al... |
Title: A New Heuristic for Feature Selection by Consistent Biclustering |
Abstract: Given a set of data, biclustering aims at finding simultaneous partitions in biclusters of its samples and of the features which are used for representing the samples. Consistent biclusterings allow to obtain correct classifications of the samples from the known classification of the features, and vice versa,... |
Title: Computational Methods in Bayesian Statistics |
Abstract: This paper focuses on utilizing two different Bayesian methods to deal with a variety of toy problems which occur in data analysis. In particular we implement the Variational Bayesian and Nested Sampling methods to tackle the problems of polynomial selection and Gaussian Mixture Models, comparing the algorith... |
Title: Linear Time Feature Selection for Regularized Least-Squares |
Abstract: We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration add... |
Title: Sliding window approach based Text Binarisation from Complex Textual images |
Abstract: Text binarisation process classifies individual pixels as text or background in the textual images. Binarization is necessary to bridge the gap between localization and recognition by OCR. This paper presents Sliding window method to binarise text from textual images with textured background. Suitable preproc... |
Title: Connected Spatial Networks over Random Points and a Route-Length Statistic |
Abstract: We review mathematically tractable models for connected networks on random points in the plane, emphasizing the class of proximity graphs which deserves to be better known to applied probabilists and statisticians. We introduce and motivate a particular statistic $R$ measuring shortness of routes in a network... |
Title: Modelling and simulating retail management practices: a first approach |
Abstract: Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail p... |
Title: Multi-Agent Simulation and Management Practices |
Abstract: Intelligent agents offer a new and exciting way of understanding the world of work. Agent-Based Simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulation models ... |
Title: Optimisation of a Crossdocking Distribution Centre Simulation Model |
Abstract: This paper reports on continuing research into the modelling of an order picking process within a Crossdocking distribution centre using Simulation Optimisation. The aim of this project is to optimise a discrete event simulation model and to understand factors that affect finding its optimal performance. Our ... |
Title: A Formal Approach to Modeling the Memory of a Living Organism |
Abstract: We consider a living organism as an observer of the evolution of its environment recording sensory information about the state space X of the environment in real time. Sensory information is sampled and then processed on two levels. On the biological level, the organism serves as an evaluation mechanism of th... |
Title: Bayesian Nonparametric Inference of Switching Linear Dynamical Systems |
Abstract: Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utiliz... |
Title: Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization |
Abstract: Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive... |
Title: On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary |
Abstract: Among the many ways to model signals, a recent approach that draws considerable attention is sparse representation modeling. In this model, the signal is assumed to be generated as a random linear combination of a few atoms from a pre-specified dictionary. In this work we analyze two Bayesian denoising algori... |
Title: The Projected GSURE for Automatic Parameter Tuning in Iterative Shrinkage Methods |
Abstract: Linear inverse problems are very common in signal and image processing. Many algorithms that aim at solving such problems include unknown parameters that need tuning. In this work we focus on optimally selecting such parameters in iterative shrinkage methods for image deblurring and image zooming. Our work us... |
Title: Colouring and breaking sticks: random distributions and heterogeneous clustering |
Abstract: We begin by reviewing some probabilistic results about the Dirichlet Process and its close relatives, focussing on their implications for statistical modelling and analysis. We then introduce a class of simple mixture models in which clusters are of different `colours', with statistical characteristics that a... |
Title: System-theoretic approach to image interest point detection |
Abstract: Interest point detection is a common task in various computer vision applications. Although a big variety of detector are developed so far computational efficiency of interest point based image analysis remains to be the problem. Current paper proposes a system-theoretic approach to interest point detection. ... |
Title: MINRES-QLP: a Krylov subspace method for indefinite or singular symmetric systems |
Abstract: CG, SYMMLQ, and MINRES are Krylov subspace methods for solving symmetric systems of linear equations. When these methods are applied to an incompatible system (that is, a singular symmetric least-squares problem), CG could break down and SYMMLQ's solution could explode, while MINRES would give a least-squares... |
Title: A Comprehensive Review of Image Enhancement Techniques |
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