831 citations · 3.2k across the 65 of their papers we have counts for
9 papers · 1 filter
Decayed MCMC Filtering
Bhaskara Marthi, Hanna Pasula, Stuart Russell +1
Filtering---estimating the state of a partially observable Markov process from a sequence of observations---is one of the most widely studied problems in control theory, AI, and co…
A Generalized Mean Field Algorithm for Variational Inference in Exponential Families
Eric P. Xing, Michael I. Jordan, Stuart Russell
The mean field methods, which entail approximating intractable probability distributions variationally with distributions from a tractable family, enjoy high efficiency, guaranteed…
Efficient Gradient Estimation for Motor Control Learning
Gregory Lawrence, Noah Cowan, Stuart Russell
The task of estimating the gradient of a function in the presence of noise is central to several forms of reinforcement learning, including policy search methods. We present two te…
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. {\em Metalevel} decision procedures have been developed for selecting…
A compact, hierarchical Q-function decomposition
Bhaskara Marthi, Stuart Russell, David Andre
Previous work in hierarchical reinforcement learning has faced a dilemma: either ignore the values of different possible exit states from a subroutine, thereby risking suboptimal b…
General-Purpose MCMC Inference over Relational Structures
Brian Milch, Stuart Russell
Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabili…