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Title: The effect of linguistic constraints on the large scale organization of language |
Abstract: This paper studies the effect of linguistic constraints on the large scale organization of language. It describes the properties of linguistic networks built using texts of written language with the words randomized. These properties are compared to those obtained for a network built over the text in natural ... |
Title: Dual-Tree Fast Gauss Transforms |
Abstract: Kernel density estimation (KDE) is a popular statistical technique for estimating the underlying density distribution with minimal assumptions. Although they can be shown to achieve asymptotic estimation optimality for any input distribution, cross-validating for an optimal parameter requires significant comp... |
Title: Decentralized Restless Bandit with Multiple Players and Unknown Dynamics |
Abstract: We consider decentralized restless multi-armed bandit problems with unknown dynamics and multiple players. 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 is passive. Players activating the same... |
Title: Compatibility of Prior Specifications Across Linear Models |
Abstract: Bayesian model comparison requires the specification of a prior distribution on the parameter space of each candidate model. In this connection two concerns arise: on the one hand the elicitation task rapidly becomes prohibitive as the number of models increases; on the other hand numerous prior specification... |
Title: Comment: Quantifying Information Loss in Survival Studies |
Abstract: Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774] |
Title: Hybrid Model for Solving Multi-Objective Problems Using Evolutionary Algorithm and Tabu Search |
Abstract: This paper presents a new multi-objective hybrid model that makes cooperation between the strength of research of neighborhood methods presented by the tabu search (TS) and the important exploration capacity of evolutionary algorithm. This model was implemented and tested in benchmark functions (ZDT1, ZDT2, a... |
Title: Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies |
Abstract: Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774] |
Title: Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies |
Abstract: Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774] |
Title: Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies |
Abstract: Rejoinder to "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774] |
Title: A Generalized Least Squares Matrix Decomposition |
Abstract: Variables in many massive high-dimensional data sets are structured, arising for example from measurements on a regular grid as in imaging and time series or from spatial-temporal measurements as in climate studies. Classical multivariate techniques ignore these structural relationships often resulting in poo... |
Title: Automated Complexity Analysis Based on the Dependency Pair Method |
Abstract: This article is concerned with automated complexity analysis of term rewrite systems. Since these systems underlie much of declarative programming, time complexity of functions defined by rewrite systems is of particular interest. Among other results, we present a variant of the dependency pair method for ana... |
Title: An Approximation Algorithm for Computing Shortest Paths in Weighted 3-d Domains |
Abstract: We present the first polynomial time approximation algorithm for computing shortest paths in weighted three-dimensional domains. Given a polyhedral domain $\D$, consisting of $n$ tetrahedra with positive weights, and a real number $\eps\in(0,1)$, our algorithm constructs paths in $\D$ from a fixed source vert... |
Title: Selecting the rank of truncated SVD by Maximum Approximation Capacity |
Abstract: Truncated Singular Value Decomposition (SVD) calculates the closest rank-$k$ approximation of a given input matrix. Selecting the appropriate rank $k$ defines a critical model order choice in most applications of SVD. To obtain a principled cut-off criterion for the spectrum, we convert the underlying optimiz... |
Title: Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data |
Abstract: We introduce a procedure to infer the interactions among a set of binary variables, based on their sampled frequencies and pairwise correlations. The algorithm builds the clusters of variables contributing most to the entropy of the inferred Ising model, and rejects the small contributions due to the sampling... |
Title: An Efficient and Integrated Algorithm for Video Enhancement in Challenging Lighting Conditions |
Abstract: We describe a novel integrated algorithm for real-time enhancement of video acquired under challenging lighting conditions. Such conditions include low lighting, haze, and high dynamic range situations. The algorithm automatically detects the dominate source of impairment, then depending on whether it is low ... |
Title: Detecting Separation in Robotic and Sensor Networks |
Abstract: In this paper we consider the problem of monitoring detecting separation of agents from a base station in robotic and sensor networks. Such separation can be caused by mobility and/or failure of the agents. While separation/cut detection may be performed by passing messages between a node and the base in stat... |
Title: Online Learning of Rested and Restless Bandits |
Abstract: In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K finite-state discrete-time Markov chains (arms) with unknown state spaces and statistics. At each time step the player can play M ... |
Title: Analytic Loss Distributional Approach Model for Operational Risk from the alpha-Stable Doubly Stochastic Compound Processes and Implications for Capital Allocation |
Abstract: Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach is not prescriptive regarding the class of statistical model utilised to undertake capital estimation. It has however become well accepted to utlise a Loss Distributional Approach (LDA) paradigm to model the individual O... |
Title: Stochastic Approximation and Newton's Estimate of a Mixing Distribution |
Abstract: Many statistical problems involve mixture models and the need for computationally efficient methods to estimate the mixing distribution has increased dramatically in recent years. Newton [Sankhya Ser. A 64 (2002) 306--322] proposed a fast recursive algorithm for estimating the mixing distribution, which we st... |
Title: Foundations for Understanding and Building Conscious Systems using Stable Parallel Looped Dynamics |
Abstract: The problem of consciousness faced several challenges for a few reasons: (a) a lack of necessary and sufficient conditions, without which we would not know how close we are to the solution, (b) a lack of a synthesis framework to build conscious systems and (c) a lack of mechanisms explaining the transition be... |
Title: Multiway Spectral Clustering: A Margin-Based Perspective |
Abstract: Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in which the relaxed solution is subsequently "rounded" into an approximate discrete solution to the original proble... |
Title: Handling Covariates in the Design of Clinical Trials |
Abstract: There has been a split in the statistics community about the need for taking covariates into account in the design phase of a clinical trial. There are many advocates of using stratification and covariate-adaptive randomization to promote balance on certain known covariates. However, balance does not always p... |
Title: A Conversation with Myles Hollander |
Abstract: Myles Hollander was born in Brooklyn, New York, on March 21, 1941. He graduated from Carnegie Mellon University in 1961 with a B.S. in mathematics. In the fall of 1961, he entered the Department of Statistics, Stanford University, earning his M.S. in statistics in 1962 and his Ph.D. in statistics in 1965. He ... |
Title: Searching in one billion vectors: re-rank with source coding |
Abstract: Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification ... |
Title: A linear framework for region-based image segmentation and inpainting involving curvature penalization |
Abstract: We present the first method to handle curvature regularity in region-based image segmentation and inpainting that is independent of initialization. To this end we start from a new formulation of length-based optimization schemes, based on surface continuation constraints, and discuss the connections to existi... |
Title: Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases |
Abstract: This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, and a new method of imputation for missing data is proposed. This methodology is applied to the DLP database rec... |
Title: Evolved preambles for MAX-SAT heuristics |
Abstract: MAX-SAT heuristics normally operate from random initial truth assignments to the variables. We consider the use of what we call preambles, which are sequences of variables with corresponding single-variable assignment actions intended to be used to determine a more suitable initial truth assignment for a give... |
Title: Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities |
Abstract: Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a... |
Title: Inferring Disease and Gene Set Associations with Rank Coherence in Networks |
Abstract: A computational challenge to validate the candidate disease genes identified in a high-throughput genomic study is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotype... |
Title: Concentration-Based Guarantees for Low-Rank Matrix Reconstruction |
Abstract: We consider the problem of approximately reconstructing a partially-observed, approximately low-rank matrix. This problem has received much attention lately, mostly using the trace-norm as a surrogate to the rank. Here we study low-rank matrix reconstruction using both the trace-norm, as well as the less-stud... |
Title: Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning |
Abstract: We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the corre... |
Title: Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection |
Abstract: We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can be viewed in the context of both feature selection and sparse approximation. We analyze the performance of widely used greedy heuri... |
Title: Privacy Preserving Spam Filtering |
Abstract: Email is a private medium of communication, and the inherent privacy constraints form a major obstacle in developing effective spam filtering methods which require access to a large amount of email data belonging to multiple users. To mitigate this problem, we envision a privacy preserving spam filtering syst... |
Title: Joint and individual variation explained (JIVE) for integrated analysis of multiple data types |
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