A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
Alice and Bob possess sequences x and y respectively and would like to compute the 1 distance, namely x - y 1 under privacy and communication constraints. The privacy constraint r...
We consider multiple description (MD) coding for the Gaussian source with K descriptions under the symmetric meansquared error (MSE) distortion constraints, and provide an approxim...