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 …

We consider the problem of sampling from a probability distribution defined over a high-
dimensional discrete set, specified for instance by a graphical model. We propose a …

Belief Propagation (BP) is an important message-passing algorithm for various reasoning
tasks over graphical models, including solving the Constraint Optimization Problems …

Abstract# SMT, or model counting for logical theories, is a well-known hard problem that
generalizes such tasks as counting the number of satisfying assignments to a Boolean …

Many probabilistic inference tasks involve summations over exponentially large sets.
Recently, it has been shown that these problems can be reduced to solving a polynomial …

Search heuristics are of paramount importance for finding good solutions to optimization
problems quickly. Manually designing problem specific search heuristics is a time …

The prediction of protein side chain conformations from backbone coordinates is an
important task in structural biology, with applications in structure prediction and protein …

We consider the task of summing a non-negative function f over a discrete set Ω, eg, to
compute the partition function of a graphical model. Ermon et al. have shown that in a …

Over-constrained problems are ubiquitous in real-world decision and optimization problems.
Plenty of modeling formalisms for various problem domains involving soft constraints have …

Despite significant successes in structure‐based computational protein design in recent
years, protein design algorithms must be improved to increase the biological accuracy of …