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Randomness Conductors and ConstantDegree LosslessExpanders
"... ABSTRACT The main concrete result of this paper is the first explicit construction of constant degree lossless expanders. In these graphs, the expansion factor is almost as large as possible: (1 s^)D, where D is the degree and s ^ is an arbitrarily small constant. The best previous explicit constru ..."
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ABSTRACT The main concrete result of this paper is the first explicit construction of constant degree lossless expanders. In these graphs, the expansion factor is almost as large as possible: (1 s^)D, where D is the degree and s ^ is an arbitrarily small constant. The best previous explicit
Nonlinear component analysis as a kernel eigenvalue problem

, 1996
"... We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all ..."
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Cited by 1554 (85 self)
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We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensorbased planning, visibility, decisiontheoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.
Unsupervised NamedEntity Extraction from the Web: An Experimental Study
 ARTIFICIAL INTELLIGENCE
, 2005
"... The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domainindependent, and scalable manner. The paper presents an overview of KNOWITALL’s novel architecture and design princip ..."
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Cited by 364 (39 self)
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The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domainindependent, and scalable manner. The paper presents an overview of KNOWITALL’s novel architecture and design principles, emphasizing its distinctive ability to extract information without any handlabeled training examples. In its first major run, KNOWITALL extracted over 50,000 facts, but suggested a challenge: How can we improve KNOWITALL’s recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domainspecific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies subclasses in order to boost recall. List Extraction locates lists of class instances, learns a “wrapper ” for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL’s domainindependent methods, the methods also obviate handlabeled training examples. The paper reports on experiments, focused on namedentity extraction, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KNOWITALL a 4fold to 8fold increase in recall, while maintaining high precision, and discovered over 10,000 cities missing from the Tipster Gazetteer.
Extractors and Pseudorandom Generators
 Journal of the ACM
, 1999
"... We introduce a new approach to constructing extractors. Extractors are algorithms that transform a "weakly random" distribution into an almost uniform distribution. Explicit constructions of extractors have a variety of important applications, and tend to be very difficult to obtain. ..."
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Cited by 113 (6 self)
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We introduce a new approach to constructing extractors. Extractors are algorithms that transform a "weakly random" distribution into an almost uniform distribution. Explicit constructions of extractors have a variety of important applications, and tend to be very difficult to obtain.
6 Randomness Extractors
"... Randomness extractors are functions that extract almostuniform bits from sources of biased and correlated bits. The original motivation for extractors was to simulate randomized algorithms with weak random sources as might arise in nature. This motivation is still compelling, but extractors have ta ..."
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of this survey, they have played a major unifying role in the theory of pseudorandomness. Indeed, the links between the various pseudorandom objects we are studying in this survey (expander graphs, randomness extractors, listdecodable codes, pseudorandom generators, samplers) were all discovered through work
6 Randomness Extractors
"... Randomness extractors are functions that extract almostuniform bits from sources of biased and correlated bits. The original motivation for extractors was to simulate randomized algorithms with weak random sources as might arise in nature. This motivation is still compelling, but extractors have ta ..."
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of this survey, they have played a major unifying role in the theory of pseudorandomness. Indeed, the links between the various pseudorandom objects we are studying in this survey (expander graphs, randomness extractors, listdecodable codes, pseudorandom generators, samplers) were all discovered through work
Extractors and rank extractors for polynomial sources
 In FOCS ’07
, 2007
"... In this paper we construct explicit deterministic extractors from polynomial sources, which are distributions sampled by low degree multivariate polynomials over finite fields. This naturally generalizes previous work on extraction from affine sources (which are degree 1 polynomials). A direct conse ..."
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Cited by 18 (8 self)
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In this paper we construct explicit deterministic extractors from polynomial sources, which are distributions sampled by low degree multivariate polynomials over finite fields. This naturally generalizes previous work on extraction from affine sources (which are degree 1 polynomials). A direct
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