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Title: Stabilizing knowledge through standards - A perspective for the humanities |
Abstract: It is usual to consider that standards generate mixed feelings among scientists. They are often seen as not really reflecting the state of the art in a given domain and a hindrance to scientific creativity. Still, scientists should theoretically be at the best place to bring their expertise into standard deve... |
Title: Adaptive Algorithms for Coverage Control and Space Partitioning in Mobile Robotic Networks |
Abstract: This paper considers deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Moreover, it is assumed that the event location d... |
Title: Assumptions of IV Methods for Observational Epidemiology |
Abstract: Instrumental variable (IV) methods are becoming increasingly popular as they seem to offer the only viable way to overcome the problem of unobserved confounding in observational studies. However, some attention has to be paid to the details, as not all such methods target the same causal parameters and some r... |
Title: Multiple View Reconstruction of Calibrated Images using Singular Value Decomposition |
Abstract: Calibration in a multi camera network has widely been studied for over several years starting from the earlier days of photogrammetry. Many authors have presented several calibration algorithms with their relative advantages and disadvantages. In a stereovision system, multiple view reconstruction is a challe... |
Title: Significance of Classification Techniques in Prediction of Learning Disabilities |
Abstract: The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities... |
Title: Lesion Border Detection 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, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in... |
Title: Parametric fitting of data obtained from detectors with finite resolution and limited acceptance |
Abstract: A goodness-of-fit test for the fitting of a parametric model to data obtained from a detector with finite resolution and limited acceptance is proposed. The parameters of the model are found by minimization of a statistic that is used for comparing experimental data and simulated reconstructed data. Numerical... |
Title: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning |
Abstract: Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees ... |
Title: Entropic Inference |
Abstract: In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induct... |
Title: Leaders, Followers, and Community Detection |
Abstract: Communities in social networks or graphs are sets of well-connected, overlapping vertices. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities and ability to scale with the size of the data. In this work, we provide three contributions. First,... |
Title: Make Research Data Public? -- Not Always so Simple: A Dialogue for Statisticians and Science Editors |
Abstract: Putting data into the public domain is not the same thing as making those data accessible for intelligent analysis. A distinguished group of editors and experts who were already engaged in one way or another with the issues inherent in making research data public came together with statisticians to initiate a... |
Title: Dempster--Shafer Theory and Statistical Inference with Weak Beliefs |
Abstract: The Dempster--Shafer (DS) theory is a powerful tool for probabilistic reasoning based on a formal calculus for combining evidence. DS theory has been widely used in computer science and engineering applications, but has yet to reach the statistical mainstream, perhaps because the DS belief functions do not sa... |
Title: Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" |
Abstract: This is a collection of discussions of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead, to appear in the Journal of the Royal Statistical Society, Series B. |
Title: A PDTB-Styled End-to-End Discourse Parser |
Abstract: We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies their relation types. When appropriate, the attribution spans to these relations are als... |
Title: Probabilistic Inferences in Bayesian Networks |
Abstract: Bayesian network is a complete model for the variables and their relationships, it can be used to answer probabilistic queries about them. A Bayesian network can thus be considered a mechanism for automatically applying Bayes' theorem to complex problems. In the application of Bayesian networks, most of the w... |
Title: Detecting Ontological Conflicts in Protocols between Semantic Web Services |
Abstract: The task of verifying the compatibility between interacting web services has traditionally been limited to checking the compatibility of the interaction protocol in terms of message sequences and the type of data being exchanged. Since web services are developed largely in an uncoordinated way, different serv... |
Title: Performance Analysis of Spectral Clustering on Compressed, Incomplete and Inaccurate Measurements |
Abstract: Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged as prevailing methods for efficiently recovering sparse and partially observed signals respectively. We combine the distance preserv... |
Title: The Lasso under Heteroscedasticity |
Abstract: The performance of the Lasso is well understood under the assumptions of the standard linear model with homoscedastic noise. However, in several applications, the standard model does not describe the important features of the data. This paper examines how the Lasso performs on a non-standard model that is mot... |
Title: Featureless 2D-3D Pose Estimation by Minimising an Illumination-Invariant Loss |
Abstract: The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing me... |
Title: Identification, Inference and Sensitivity Analysis for Causal Mediation Effects |
Abstract: Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. In this p... |
Title: Particle Learning and Smoothing |
Abstract: Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics ... |
Title: The Importance of Scale for Spatial-Confounding Bias and Precision of Spatial Regression Estimators |
Abstract: Residuals in regression models are often spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple... |
Title: Multiarmed Bandit Problems with Delayed Feedback |
Abstract: In this paper we initiate the study of optimization of bandit type problems in scenarios where the feedback of a play is not immediately known. This arises naturally in allocation problems which have been studied extensively in the literature, albeit in the absence of delays in the feedback. We study this pro... |
Title: Interacting Multiple Try Algorithms with Different Proposal Distributions |
Abstract: We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing... |
Title: Coupling optional P\'olya trees and the two sample problem |
Abstract: Testing and characterizing the difference between two data samples is of fundamental interest in statistics. Existing methods such as Kolmogorov-Smirnov and Cramer-von-Mises tests do not scale well as the dimensionality increases and provides no easy way to characterize the difference should it exist. In this... |
Title: Privately Releasing Conjunctions and the Statistical Query Barrier |
Abstract: Suppose we would like to know all answers to a set of statistical queries C on a data set up to small error, but we can only access the data itself using statistical queries. A trivial solution is to exhaustively ask all queries in C. Can we do any better? + We show that the number of statistical queries nece... |
Title: The Loss Rank Criterion for Variable Selection in Linear Regression Analysis |
Abstract: Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularization algorithm, a consistent model selection criterion is proposed to select the best one among this preselected ... |
Title: Model Selection by Loss Rank for Classification and Unsupervised Learning |
Abstract: Hutter (2007) recently introduced the loss rank principle (LoRP) as a generalpurpose principle for model selection. The LoRP enjoys many attractive properties and deserves further investigations. The LoRP has been well-studied for regression framework in Hutter and Tran (2010). In this paper, we study the LoR... |
Title: Gradient Computation In Linear-Chain Conditional Random Fields Using The Entropy Message Passing Algorithm |
Abstract: The paper proposes a numerically stable recursive algorithm for the exact computation of the linear-chain conditional random field gradient. It operates as a forward algorithm over the log-domain expectation semiring and has the purpose of enhancing memory efficiency when applied to long observation sequences... |
Title: Robust Matrix Decomposition with Outliers |
Abstract: Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from the observed sum. Such additive decompositions have applications in a variety of numerical problems including system identification, ... |
Title: Power-law Distributions in Information Science - Making the Case for Logarithmic Binning |
Abstract: We suggest partial logarithmic binning as the method of choice for uncovering the nature of many distributions encountered in information science (IS). Logarithmic binning retrieves information and trends "not visible" in noisy power-law tails. We also argue that obtaining the exponent from logarithmically bi... |
Title: Online Importance Weight Aware Updates |
Abstract: An importance weight quantifies the relative importance of one example over another, coming up in applications of boosting, asymmetric classification costs, reductions, and active learning. The standard approach for dealing with importance weights in gradient descent is via multiplication of the gradient. We ... |
Title: Asymptotic Synchronization for Finite-State Sources |
Abstract: We extend a recent synchronization analysis of exact finite-state sources to nonexact sources for which synchronization occurs only asymptotically. Although the proof methods are quite different, the primary results remain the same. We find that an observer's average uncertainty in the source state vanishes e... |
Title: Reinforcement Learning Based on Active Learning Method |
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