2010 IEEE 51st Annual Symposium on Foundations of Computer Science

23-26 Oct. 2010

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  • [Front cover]

    Publication Year: 2010, Page(s): C1
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  • [Title page i]

    Publication Year: 2010, Page(s): i
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  • [Title page iii]

    Publication Year: 2010, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2010, Page(s): iv
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  • Table of contents

    Publication Year: 2010, Page(s):v - x
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  • Foreword

    Publication Year: 2010, Page(s): xii
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  • Organizing Committee

    Publication Year: 2010, Page(s): xiii
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  • Program Committee

    Publication Year: 2010, Page(s): xiv
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  • Reviewers

    Publication Year: 2010, Page(s):xv - xvi
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  • Constructive Algorithms for Discrepancy Minimization

    Publication Year: 2010, Page(s):3 - 10
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (566 KB) | HTML iconHTML

    Given a set system (V, S), V = {1,..., n} and S = {S1,...,Sm}, the minimum discrepancy problem is to find a 2-coloring X : V → {-1,+1}, such that each set is colored as evenly as possible, i.e. find X to minimize maxj∈|m] Σi∈sj X(i)|· In this paper we give the first polynomial time algorithms for discrepancy minimizatio... View full abstract»

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  • Bounded Independence Fools Degree-2 Threshold Functions

    Publication Year: 2010, Page(s):11 - 20
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (643 KB) | HTML iconHTML

    For an n-variate degree-2 real polynomial p, we prove that Ex~D[sig(p(x))] Is determined up to an additive ε as long as D is a k-wise Independent distribution over {-1, 1}n for k = poly(1/ε). This gives a broad class of explicit pseudorandom generators against degree-2 boolean threshold functions, and answers an open question of Diakonikolas et al. (FOCS 2009). View full abstract»

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  • From Sylvester-Gallai Configurations to Rank Bounds: Improved Black-Box Identity Test for Depth-3 Circuits

    Publication Year: 2010, Page(s):21 - 29
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (380 KB) | HTML iconHTML

    We study the problem of identity testing for depth-3 circuits of top fanin k and degree d. We give a new structure theorem for such identities. A direct application of our theorem improves the known deterministic d -time black-box identity test over rationals (Kayal & Saraf, FOCS 2009) to one that takes d(O(k2))-time. Our structure theorem essentially says that the number of indepen... View full abstract»

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  • The Coin Problem and Pseudorandomness for Branching Programs

    Publication Year: 2010, Page(s):30 - 39
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (385 KB) | HTML iconHTML

    The Coin Problem is the following problem: a coin is given, which lands on head with probability either 1/2 + β or 1/2 - β. We are given the outcome of n independent tosses of this coin, and the goal is to guess which way the coin is biased, and to answer correctly with probability ≥ 2/3. When our computational model is unrestricted, the majority function is optimal, and succe... View full abstract»

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  • Pseudorandom Generators for Regular Branching Programs

    Publication Year: 2010, Page(s):40 - 47
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (434 KB) | HTML iconHTML

    We give new pseudorandom generators for regular read-once branching programs of small width. A branching program is regular if the in-degree of every vertex in it is either 0 or 2. For every width d and length n, our pseudorandom generator uses a seed of length O((log d + log log n + log(1/ϵ)) log n) to produce n bits that cannot be distinguished from a uniformly random string by any regula... View full abstract»

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  • Boosting and Differential Privacy

    Publication Year: 2010, Page(s):51 - 60
    Cited by:  Papers (39)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (489 KB) | HTML iconHTML

    Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacy-pre serving synopses of an input database. These are data structures that yield, for a given set Q of queries over an input database, reasonably accurate estimates of the responses to every query in Q, even when the number of queries is much larger than the number of rows i... View full abstract»

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  • A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis

    Publication Year: 2010, Page(s):61 - 70
    Cited by:  Papers (34)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (405 KB) | HTML iconHTML

    We consider statistical data analysis in the interactive setting. In this setting a trusted curator maintains a database of sensitive information about individual participants, and releases privacy-preserving answers to queries as they arrive. Our primary contribution is a new differentially private multiplicative weights mechanism for answering a large number of interactive counting (or linear) q... View full abstract»

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  • Impossibility of Differentially Private Universally Optimal Mechanisms

    Publication Year: 2010, Page(s):71 - 80
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (523 KB) | HTML iconHTML

    The notion of universally utility-maximizing privacy mechanism was recently introduced by Ghosh, Rough garden, and Sundararajan [STOC 2009]. These are mechanisms that guarantee optimal utility to a large class of information consumers, simultaneously, while preserving Differential Privacy [Dwork, McSherry, Nissim, and Smith, TCC 2006]. Ghosh, Rough garden and Sundararajan have demonstrated, quite ... View full abstract»

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  • The Limits of Two-Party Differential Privacy

    Publication Year: 2010, Page(s):81 - 90
    Cited by:  Papers (19)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (549 KB) | HTML iconHTML

    We study differential privacy in a distributed setting where two parties would like to perform analysis of their joint data while preserving privacy for both datasets. Our results imply almost tight lower bounds on the accuracy of such data analyses, both for specific natural functions (such as Hamming distance) and in general. Our bounds expose a sharp contrast between the two-party setting and t... View full abstract»

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  • Settling the Polynomial Learnability of Mixtures of Gaussians

    Publication Year: 2010, Page(s):93 - 102
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (816 KB) | HTML iconHTML

    Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has running time and data requirements polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians. As a simple consequence of our learning algorithm, we we give the first ... View full abstract»

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  • Polynomial Learning of Distribution Families

    Publication Year: 2010, Page(s):103 - 112
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (501 KB) | HTML iconHTML

    The question of polynomial learn ability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and machine learning. However, despite major progress, the general question of polynomial learn ability of Gaussian mixture distributions still remained open. The current work resolves the question of polynom... View full abstract»

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  • Agnostically Learning under Permutation Invariant Distributions

    Publication Year: 2010, Page(s):113 - 122
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (427 KB) | HTML iconHTML

    We generalize algorithms from computational learning theory that are successful under the uniform distribution on the Boolean hypercube {0,1}n to algorithms successful on permutation invariant distributions. A permutation invariant distribution is a distribution where the probability mass remains constant upon permutations in the instances. While the tools in our generalization mimic th... View full abstract»

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  • Corrigendum: A Random Sampling Algorithm for Learning an Intersection of Halfspaces

    Publication Year: 2010, Page(s): 123
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (257 KB) | HTML iconHTML

    We correct a claim from [Vem97] and provide a status update. View full abstract»

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  • Learning Convex Concepts from Gaussian Distributions with PCA

    Publication Year: 2010, Page(s):124 - 130
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (361 KB) | HTML iconHTML

    We present a new algorithm for learning a convex set in n-dimensional space given labeled examples drawn from any Gaussian distribution. The complexity of the algorithm is bounded by a fixed polynomial in n times a function of k and ϵ where k is the dimension of the normal subspace (the span of normal vectors to supporting hyperplanes of the convex set) and the output is a hypothesis that c... View full abstract»

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  • Deciding First-Order Properties for Sparse Graphs

    Publication Year: 2010, Page(s):133 - 142
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (416 KB) | HTML iconHTML

    We present a linear-time algorithm for deciding first-order logic (FOL) properties in classes of graphs with bounded expansion. Many natural classes of graphs have bounded expansion: graphs of bounded tree-width, all proper minor-closed classes of graphs, graphs of bounded degree, graphs with no sub graph isomorphic to a subdivision of a fixed graph, and graphs that can be drawn in a fixed surface... View full abstract»

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  • Logspace Versions of the Theorems of Bodlaender and Courcelle

    Publication Year: 2010, Page(s):143 - 152
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (449 KB) | HTML iconHTML

    Bodlaender's Theorem states that for every k there is a linear-time algorithm that decides whether an input graph has tree width k and, if so, computes a width-k tree composition. Courcelle's Theorem builds on Bodlaender's Theorem and states that for every monadic second-order formula φ and for every k there is a linear-time algorithm that decides whether a given logical structure A of tree... View full abstract»

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