Vazirani, Umesh VirkumarOverview
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Most widely held works by
Umesh Virkumar Vazirani
An introduction to computational learning theory
by Michael J Kearns
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16 editions published between 1994 and 1997 in English and held by 1,186 WorldCat member libraries worldwide Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs.The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the VapnikChervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation
Algorithms
by Sanjoy Dasgupta
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12 editions published between 2006 and 2011 in English and held by 233 WorldCat member libraries worldwide
Probabilistic combinatorics and its applications
by Béla Bollobás
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1 edition published in 1991 in English and held by 10 WorldCat member libraries worldwide
Simple and efficient leader election in the full information model
by Rafail Ostrovsky
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Book
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2 editions published in 1994 in English and held by 5 WorldCat member libraries worldwide
A mildly exponential approximation algorithm for the permanent
by Mark Jerrum
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2 editions published in 1991 in English and held by 4 WorldCat member libraries worldwide Abstract: "A new approximation algorithm for the permanent of an n x n 0,1matrix is presented. The algorithm is shown to have worstcase time complexity exp(O(n[superscript 1/2] log²n)). Asymptotically, this represents a considerable improvement over the best existing algorithm, which has worstcase time complexity of the form e[superscipt theta(n)]."
The twoprocessor scheduling problem is in random NC
by Umesh Virkumar Vazirani
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Book
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2 editions published in 1989 in English and held by 4 WorldCat member libraries worldwide Abstract: "An efficient parallel algorithm (RNC[superscript 2]) for the twoprocessor scheduling problem is presented. An interesting feature of this algorithm is that it finds a highest level first schedule: such a schedule defines a lexicographically first solution to this problem in a natural way. A key ingredient of the algorithm is a generalization of a theorem of Tutte which establishes a onetoone correspondence between the bases of the Tutte matrix of a graph and the sets of matched nodes in maximum matchings in the graph."
Random polynomial time is equal to semirandom polynomial time
by Umesh Virkumar Vazirani
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Book
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2 editions published in 1988 in English and held by 4 WorldCat member libraries worldwide We prove that any onesided error random polynomial time (RP) algorithm can be simulated with a semirandom source at no more than polynomial factor loss in efficiency. i.e. RP=SRP. This contrasts with the fact that a semirandom source is too weak to simulate fair coin flips [SV]
Conductance and convergence of Markov chains
by Milena Mihail
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Book
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2 editions published in 1989 in English and held by 3 WorldCat member libraries worldwide Abstract: "Let P be an irreducible and strongly aperiodic (i.e. p[subscript ii] [greater than or equal to] 1/2 [for all] i) stochastic matrix. We obtain nonasymptotic bounds for the convergence rate of a Markov chain with transition matrix P in terms of the conductance of P. These results have been so far obtained only for timereversible Markov chains via partially linear algebraic arguments [Al86][A87][SJ87]. Our proofs eliminate the linear algebra and therefore naturally extend to general Markov chains. The key new idea is to view the action of a strongly aperiodic stochastic matrix as a weighted averaging along the edges of the underlying graph of P. Our results suggest that the conductance (rather than the second largest eigenvalue) best quantifies the rate of convergence of stongly aperiodic Markov chains."
Algorytmy
by Sanjoy Dasgupta
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Book
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2 editions published between 2010 and 2012 in Polish and held by 3 WorldCat member libraries worldwide
On the magnification of 01 polytopes
by Milena Mihail
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Book
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2 editions published between 1988 and 1989 in English and held by 3 WorldCat member libraries worldwide
NC algorithms for comparability graphs, interval graphs, and unique perfect matchings
by Dexter Kozen
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3 editions published in 1986 in English and Undetermined and held by 3 WorldCat member libraries worldwide
Suan fa gai lun
by Sanjoy Dasgupta
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1 edition published in 2008 in Chinese and held by 2 WorldCat member libraries worldwide
Randomness, adversaries and computation
by Umesh Virkumar Vazirani
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Book
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1 edition published in 1986 in English and held by 2 WorldCat member libraries worldwide
An optimal algorithm for online bipartite matching
by Richard M Karp
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Book
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1 edition published in 1991 in English and held by 2 WorldCat member libraries worldwide
Algoritmos
by Sanjoy Dasgupta
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1 edition published in 2010 in Portuguese and held by 1 WorldCat member library worldwide
Scalability, Complexity and Reliability in Quantum Information Processing
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1 edition published in 2007 in English and held by 1 WorldCat member library worldwide This program was a theoretical/experimental collaboration focusing on the fundamental need for scalability in the development of quantum information processing. Theory and experiment were connected and interleaved at several levels. The theoretical objectives were as follows: (1) to develop new theoretical tools to enable the implementation of reliable quantum information processing in scalable systems; and (2) to characterize the relations between quantum algorithms and architectures, fault tolerance and architectures, and quantum and classical complexity classes; and (3) to develop secure primitives for quantum cryptography. The experimental objectives were to develop a scalable quantum component technology based on gas phase systems using atoms and light fields. Specific experimental goals were scalable implementation of universal quantum logic in optical lattices and the achievement of deterministic control in atom/cavity systems
Quantum Information Processing
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1 edition published in 2004 in English and held by 1 WorldCat member library worldwide This effort supported a Quantum Information Workshop at the University of California, Berkeley. After a cut in funding, the remaining unspent funding was used to support: (I) research related to the internet involving the interplay between mechanism design and computational complexity, (2) improved understanding of the connections between phase transitions in statistical mechanics and mixing times of Markov chain Monte Carlo algorithms, and (3) an investigation of the logic of transcriptional control of various model organisms, with emphasis on characterizing the binding sites of key transcription factors via stochastic models
Matching ia as easy as matrix inversion
by Ketan Mulmuley
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Book
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2 editions published in 1986 in English and held by 1 WorldCat member library worldwide more
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Associated Subjects
Algorithms Approximation theory Artificial intelligence Combinatorial probabilities Computer algorithms Computer science Convex polytopes Distributed parameter systems Graph theory Machine learning Markov processes Neural networks (Computer science) Parallel processing (Electronic computers) Production scheduling Random graphs Stochastic matrices

Alternative Names
Vazirani, Umesh.
Vazirani, Umesh V.
Vazirani, Umesh Virkumar.
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