{"title":"An empirical comparison of sampling techniques for matrix column subset selection","authors":"Yining Wang, Aarti Singh","doi":"10.1109/ALLERTON.2015.7447127","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447127","url":null,"abstract":"Column subset selection (CSS) is the problem of selecting a small portion of columns from a large data matrix as one form of interpretable data summarization. Leverage score sampling, which enjoys both sound theoretical guarantee and superior empirical performance, is widely recognized as the state-of-the-art algorithm for column subset selection. In this paper, we revisit iterative norm sampling, another sampling based CSS algorithm proposed even before leverage score sampling, and demonstrate its competitive performance under a wide range of experimental settings. We also compare iterative norm sampling with several of its other competitors and show its superior performance in terms of both approximation accuracy and computational efficiency. We conclude that further theoretical investigation and practical consideration should be devoted to iterative norm sampling in column subset selection.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Finite-time analysis of the distributed detection problem","authors":"Shahin Shahrampour, A. Rakhlin, A. Jadbabaie","doi":"10.1109/ALLERTON.2015.7447059","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447059","url":null,"abstract":"This paper addresses the problem of distributed detection in fixed and switching networks. A network of agents observe partially informative signals about the unknown state of the world. Hence, they collaborate with each other to identify the true state. We propose an update rule building on distributed, stochastic optimization methods. Our main focus is on the finite-time analysis of the problem. For fixed networks, we bring forward the notion of Kullback-Leibler cost to measure the efficiency of the algorithm versus its centralized analog. We bound the cost in terms of the network size, spectral gap and relative entropy of agents' signal structures. We further consider the problem in random networks where the structure is realized according to a stationary distribution. We then prove that the convergence is exponentially fast (with high probability), and the non-asymptotic rate scales inversely in the spectral gap of the expected network.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114168504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the rate of learning in distributed hypothesis testing","authors":"Anusha Lalitha, T. Javidi","doi":"10.1109/ALLERTON.2015.7446979","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7446979","url":null,"abstract":"This paper considers a problem of distributed hypothesis testing and cooperative learning. Individual nodes in a network receive noisy local (private) observations whose distribution is parameterized by a discrete parameter (hypotheses). The conditional distributions are known locally at the nodes, but the true parameter/hypothesis is not known. We consider a social (“non-Bayesian”) learning rule from previous literature, in which nodes first perform a Bayesian update of their belief (distribution estimate) of the parameter based on their local observation, communicate these updates to their neighbors, and then perform a “non-Bayesian” linear consensus using the log-beliefs of their neighbors. For this learning rule, we know that under mild assumptions, the belief of any node in any incorrect parameter converges to zero exponentially fast, and the exponential rate of learning is a characterized by the network structure and the divergences between the observations' distributions. Tight bounds on the probability of deviating from this nominal rate in aperiodic networks is derived. The bounds are shown to hold for all conditional distributions which satisfy a mild bounded moment condition.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116136979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sparse covariance estimation based on sparse-graph codes","authors":"Ramtin Pedarsani, Kangwook Lee, K. Ramchandran","doi":"10.1109/allerton.2015.7447061","DOIUrl":"https://doi.org/10.1109/allerton.2015.7447061","url":null,"abstract":"We consider the problem of recovering a sparse covariance matrix Σ∈ℝn×n from m quadratic measurements yi = aiTΣai+wi, 1 ≤ i ≤ m, where ai ∈ ℓn is a measurement vector and wi is additive noise. We assume that ℝ has K non-zero off-diagonal entries. We first consider the simplified noiseless problem where wi = 0 for all i. We introduce two low complexity algorithms, the first a “message-passing” algorithm and the second a “forward” algorithm, that are based on a sparse-graph coding framework. We show that under some simplifying assumptions, the message passing algorithm can recover an arbitrarily-large fraction of the K non-zero components with cK measurements, where c is a small constant that can be precisely characterized. As one instance, the message passing algorithm can recover, with high probability, a fraction 1 - 10-4 of the non-zero components, using only m = 6K quadratic measurements, which is a small constant factor from the fundamental limit, with an optimal O(K) decoding complexity. We further show that the forward algorithm can recover all the K non-zero entries with high probability with m = Θ(K) measurements and O(K log(K)) decoding complexity. However, the forward algorithm suffers from significantly larger constants in terms of the number of required measurements, and is indeed less practical despite providing stronger theoretical guarantees. We then consider the noisy setting, and show that both proposed algorithms can be robustified to noise with m = Θ(K log2(n)) measurements. Finally, we provide extensive simulation results that support our theoretical claims.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115295019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Are generalized cut-set bounds tight for the deterministic interference channel?","authors":"Mehrdad Kiamari, A. Avestimehr","doi":"10.1109/ALLERTON.2015.7447176","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447176","url":null,"abstract":"We propose the idea of extended networks, which is constructed by replicating the users in the two-user deterministic interference channel (DIC) and designing the interference structure among them, such that any rate that can be achieved by each user in the original network can also be achieved simultaneously by all replicas of that user in the extended network. We demonstrate that by carefully designing extended networks and applying the generalized cut-set (GCS) bound to them, we can derive a tight converse for the two-user DIC. Furthermore, we generalize our techniques to the three-user DIC, and demonstrate that the proposed approach also results in deriving a tight converse for the three-user DIC in the symmetric case.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116750671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical and computational guarantees for the Baum-Welch algorithm","authors":"Fanny Yang, Sivaraman Balakrishnan, M. Wainwright","doi":"10.1109/ALLERTON.2015.7447067","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447067","url":null,"abstract":"The Hidden Markov Model (HMM) is one of the main-stays of statistical modeling of discrete time series and is widely used in many applications. Estimating an HMM from its observation process is often addressed via the Baum-Welch algorithm, which performs well empirically when initialized reasonably close to the truth. This behavior could not be explained by existing theory which predicts susceptibility to bad local optima. In this paper we aim at closing the gap and provide a framework to characterize a sufficient basin of attraction for any global optimum in which Baum-Welch is guaranteed to converge linearly to an “optimally” small ball around the global optimum. The framework is then used to determine the linear rate of convergence and a sufficient initialization region for Baum-Welch applied on a two component isotropic hidden Markov mixture of Gaussians.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122674305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimal multi-vehicle adaptive search with entropy objectives","authors":"Huanyu Ding, D. Castañón","doi":"10.1109/ALLERTON.2015.7447085","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447085","url":null,"abstract":"The problem of searching for an unknown object occurs in important applications, ranging from security, medicine and defense. Modern sensors have significant processing capabilities that allow for in situ processing and exploitation of the information to select what additional information to collect. In this paper, we discuss a class of dynamic, adaptive search problems involving multiple sensors sensing for a single stationary object, and formulate them as stochastic control problems with imperfect information. The objective of these problems is related to information entropy. This allows for a complete characterization of the optimal strategies and the optimal cost for the resulting finite-horizon stochastic control problems. We show that the computation of optimal policies can be reduced to solving a finite number of strictly concave maximization problems. We further show that the solution can be decoupled into a finite number of scalar concave maximization problems. We illustrate our results with experiments using multiple sensors searching for a single object.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132745198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Is the direction of greater Granger causal influence the same as the direction of information flow?","authors":"Praveen Venkatesh, P. Grover","doi":"10.1109/ALLERTON.2015.7447069","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447069","url":null,"abstract":"Granger causality is an established statistical measure of the “causal influence” that one stochastic process X has on another process Y. Along with its more recent generalization - Directed Information - Granger Causality has been used extensively in neuroscience, and in complex interconnected systems in general, to infer statistical causal influences. More recently, many works compare the Granger causality metrics along forward and reverse links (from X to Y and from Y to X), and interpret the direction of greater causal influence as the “direction of information flow”. In this paper, we question whether the direction yielded by comparing Granger Causality or Directed Information along forward and reverse links is always the same as the direction of information flow. We explore this question using two simple theoretical experiments, in which the true direction of information flow (the “ground truth”) is known by design. The experiments are based on a communication system with a feedback channel, and employ a strategy inspired by the work of Schalkwijk and Kailath. We show that in these experiments, the direction of information flow can be opposite to the direction of greater Granger causal influence or Directed Information. We also provide information-theoretic intuition for why such counterexamples are not surprising, and why Granger causality-based information-flow inferences will only get more tenuous in larger networks. We conclude that one must not use comparison/difference of Granger causality to infer the direction of information flow.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133970165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the representability of integer polymatroids: Applications in linear code construction","authors":"Amir Salimi, M. Médard, Shuguang Cui","doi":"10.1109/ALLERTON.2015.7447046","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447046","url":null,"abstract":"It has been shown that there is a duality between the linear network coding solution and the entropic vectors induced by collection of subspaces in a vector space over a finite field (dubbed linearly constructed entropic vectors). The region of all linearly constructed vectors, coincides with the set of all representable polymatroids. For any integer polymatroid, there is an associated matroid, which uniquely identifies the polymatroid. We conjecture that the representability of the underlying matroid is a sufficient condition for integer polymatroids to be linearly representable. We prove that the conjecture holds for representation over real numbers. Furthermore, we show that any real-valued submodular function (such as Shannon entropy) can be approximated (arbitrarily close) by an integer polymatroid.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124497393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Information-theoretic private interactive mechanism","authors":"Bahman Moraffah, L. Sankar","doi":"10.1109/ALLERTON.2015.7447104","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447104","url":null,"abstract":"An information-theoretic mechanism for privacy-guaranteed interactions is introduced between two memoryless correlated sources where each source is characterized by a pair of public and private variables. The interactions are modeled as a collection of K/2 pairs of random mappings, one pair for each of the K rounds of interactions. The K/2 random mapping pairs are chosen jointly to minimize the information leakage (privacy measure) over K rounds of the private variable of each source at the other source while ensuring that a desired measure of utility (distortion) of the revealed public variable is satisfied. Arguing that an average case information-theoretic privacy metric can be appropriate for streaming data settings, this paper shows that in general, interaction reduces privacy leakage by drawing some parallels between this problem and the classic interactive source coding problem. Specifically, for the log-loss distortion metric it is shown that the resulting interaction problem is an analog of an interactive information bottleneck problem for which a one-shot interactive mechanism is, in general, not optimal. For the resulting problem with a non-convex constraint space, an algorithm that extends the one-way agglomerative information bottleneck algorithm to the interactive setting is introduced.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115792496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}