most citedA Bayesian Approach to Tackling Hard Computational Problems

116 citations · 239 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG201376 cited

Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization

Stefano Ermon, Carla P. Gomes, Ashish Sabharwal +1

Integration is affected by the curse of dimensionality and quickly becomes intractable as the dimensionality of the problem grows. We propose a randomized algorithm that, with high…

cs.AI20136 cited

Algorithm Portfolio Design: Theory vs. Practice

Carla P. Gomes, Bart Selman

Stochastic algorithms are among the best for solving computationally hard search and reasoning problems. The runtime of such procedures is characterized by a random variable. Diffe…

cs.AI2013116 cited

A Bayesian Approach to Tackling Hard Computational Problems

Eric J. Horvitz, Yongshao Ruan, Carla P. Gomes +3

We are developing a general framework for using learned Bayesian models for decision-theoretic control of search and reasoningalgorithms. We illustrate the approach on the specific…

cs.AI201240 cited

Uniform Solution Sampling Using a Constraint Solver As an Oracle

Stefano Ermon, Carla P. Gomes, Bart Selman

We consider the problem of sampling from solutions defined by a set of hard constraints on a combinatorial space. We propose a new sampling technique that, while enforcing a unifor…

cs.AI20121 cited

Survey Propagation Revisited

Lukas Kroc, Ashish Sabharwal, Bart Selman

Survey propagation (SP) is an exciting new technique that has been remarkably successful at solving very large hard combinatorial problems, such as determining the satisfiability o…