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Results 1 - 10
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800
Improved Algorithms via Approximations of Probability Distributions
- Journal of Computer and System Sciences
, 1997
"... We present two techniques for approximating probability distributions. The first is a simple method for constructing the small-bias probability spaces introduced by Naor & Naor. We show how to efficiently combine this construction with the method of conditional probabilities to yield improved NC ..."
Abstract
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Cited by 25 (4 self)
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We present two techniques for approximating probability distributions. The first is a simple method for constructing the small-bias probability spaces introduced by Naor & Naor. We show how to efficiently combine this construction with the method of conditional probabilities to yield improved
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations
"... We present a novel message passing algorithm for approximating the MAP problem in graphical models. The algorithm is similar in structure to max-product but unlike max-product it always converges, and can be proven to find the exact MAP solution in various settings. The algorithm is derived via bloc ..."
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Cited by 160 (14 self)
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We present a novel message passing algorithm for approximating the MAP problem in graphical models. The algorithm is similar in structure to max-product but unlike max-product it always converges, and can be proven to find the exact MAP solution in various settings. The algorithm is derived via
Boosted sampling: Approximation algorithms for stochastic optimization problems
- IN: 36TH STOC
, 2004
"... Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the STEINER TREE problem, for example, edges must be chosen to connect terminals (clients); in VERTEX COVER, vertices must be chosen t ..."
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Cited by 98 (23 self)
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factor of σ> 1. The goal is to minimize the first stage cost plus the expected second stage cost. We give a general yet simple technique to adapt approximation algorithms for several deterministic problems to their stochastic versions via the following method. • First stage: Draw σ independent sets
Optimal Approximation for the Submodular Welfare Problem in the value oracle model
- STOC'08
, 2008
"... In the Submodular Welfare Problem, m items are to be distributed among n players with utility functions wi: 2 [m] → R+. The utility functions are assumed to be monotone and submodular. Assuming that player i receives a set of items Si, we wish to maximize the total utility Pn i=1 wi(Si). In this pap ..."
Abstract
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Cited by 123 (13 self)
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], a (1 − 1/e)approximation has been achieved and this is optimal for these problems in the value oracle model [22, 6, 15]. A (1 − 1/e)-approximation for the general Submodular Welfare Problem has been known only in a stronger demand oracle model [4], where in fact 1 − 1/e can be improved [9
An Interruptible Algorithm for Perfect Sampling via Markov Chains
- Annals of Applied Probability
, 1998
"... For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution # on an enormously large state space, but elementary sampling is ruled out by the infeasibility of calculating an appropriate nor ..."
Abstract
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Cited by 94 (7 self)
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For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution # on an enormously large state space, but elementary sampling is ruled out by the infeasibility of calculating an appropriate
Truthful and Near-Optimal Mechanism Design via Linear Programming
"... We give a general technique to obtain approximation mechanisms that are truthful in expectation.We show that for packing domains, any ff-approximation algorithm that also bounds the integrality gapof the LP relaxation of the problem by ff can be used to construct an ff-approximation mechanismthat is ..."
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Cited by 134 (12 self)
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We give a general technique to obtain approximation mechanisms that are truthful in expectation.We show that for packing domains, any ff-approximation algorithm that also bounds the integrality gapof the LP relaxation of the problem by ff can be used to construct an ff-approximation mechanismthat
Sampling from large matrices: an approach through geometric functional analysis
- Journal of the ACM
, 2006
"... Abstract. We study random submatrices of a large matrix A. We show how to approximately compute A from its random submatrix of the smallest possible size O(r log r) with a small error in the spectral norm, where r = �A�2 F /�A�22 is the numerical rank of A. The numerical rank is always bounded by, a ..."
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Cited by 132 (5 self)
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slight improvement on the best known sample complexity for an approximation algorithm for MAX-2CSP problems. We use methods of Probability in Banach spaces, in particular the law of large numbers for operator-valued random variables. 1.
A fast randomized algorithm for the approximation of matrices
, 2007
"... We introduce a randomized procedure that, given an m×n matrix A and a positive integer k, approximates A with a matrix Z of rank k. The algorithm relies on applying a structured l × m random matrix R to each column of A, where l is an integer near to, but greater than, k. The structure of R allows u ..."
Abstract
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Cited by 63 (7 self)
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We introduce a randomized procedure that, given an m×n matrix A and a positive integer k, approximates A with a matrix Z of rank k. The algorithm relies on applying a structured l × m random matrix R to each column of A, where l is an integer near to, but greater than, k. The structure of R allows
Approximate Classification via Earthmover Metrics
- In SODA ’04: Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
, 2004
"... Given a metric space (X, d), a natural distance measure on probability distributions over X is the earthmover metric. We use randomized rounding of earthmover metrics to devise new approximation algorithms for two well-known classification problems, namely, metric labeling and 0-extension. ..."
Abstract
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Cited by 20 (3 self)
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Given a metric space (X, d), a natural distance measure on probability distributions over X is the earthmover metric. We use randomized rounding of earthmover metrics to devise new approximation algorithms for two well-known classification problems, namely, metric labeling and 0-extension.
Improving the Mean Field Approximation via the Use of Mixture Distributions
, 1998
"... Introduction Graphical models provide a formalism in which to express and manipulate conditional independence statements. Inference algorithms for graphical models exploit these independence statements, using them to compute conditional probabilities while avoiding brute force marginalization over ..."
Abstract
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Cited by 50 (1 self)
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Introduction Graphical models provide a formalism in which to express and manipulate conditional independence statements. Inference algorithms for graphical models exploit these independence statements, using them to compute conditional probabilities while avoiding brute force marginalization over
Results 1 - 10
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800
