activity
20122017
most citedRAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains

17 citations · 59 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG20175 cited

Signal-based Bayesian Seismic Monitoring

David A. Moore, Stuart J. Russell

Detecting weak seismic events from noisy sensors is a difficult perceptual task. We formulate this task as Bayesian inference and propose a generative model of seismic events and s…

cs.AI201411 cited

Selecting Computations: Theory and Applications

Nicholas Hay, Stuart Russell, David Tolpin +1

Sequential decision problems are often approximately solvable by simulating possible future action sequences. Metalevel decision procedures have been developed for selecting which…

cs.LG20122 cited

Graph partition strategies for generalized mean field inference

Eric P. Xing, Michael I. Jordan, Stuart Russell

An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well…

cs.AI2012

Improving Gradient Estimation by Incorporating Sensor Data

Gregory Lawrence, Stuart Russell

An efficient policy search algorithm should estimate the local gradient of the objective function, with respect to the policy parameters, from as few trials as possible. Whereas mo…

cs.AI201217 cited

RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains

Emma Brunskill, Stuart Russell

Despite the intractability of generic optimal partially observable Markov decision process planning, there exist important problems that have highly structured models. Previous res…

cs.AI201213 cited

Gibbs Sampling in Open-Universe Stochastic Languages

Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth +1

Languages for open-universe probabilistic models (OUPMs) can represent situations with an unknown number of objects and iden- tity uncertainty. While such cases arise in a wide ran…