We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Part...
We develop a hierarchical approach to planning for partially observable Markov decision processes (POMDPs) in which a policy is represented as a hierarchical finite-state control...
Segmenting brain from non-brain tissue within magnetic resonance (MR) images of the human head, also known as skull-stripping, is a critical processing step in the analysis of neu...
Gautam Prasad, Anand A. Joshi, Paul M. Thompson, A...
Abstract—Detecting and localizing performance faults is crucial for operating large enterprise data centers. This problem is relatively straightforward to solve if each entity (a...
Vaishali P. Sadaphal, Maitreya Natu, Harrick M. Vi...
We investigate methods for planning in a Markov Decision Process where the cost function is chosen by an adversary after we fix our policy. As a running example, we consider a rob...
H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum