Programs to solve so-called constraint problems are complex pieces of software which require many design decisions to be made more or less arbitrarily by the implementer. These dec...
The automatic tuning of the parameters of algorithms and automatic selection of algorithms has received a lot of attention recently. One possible approach is the use of machine lea...
In this paper we study how to improve nearest neighbor classification by learning a Mahalanobis distance metric. We build on a recently proposed framework for distance metric lear...
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
Marek A. Perkowski, Alan Mishchenko, Anatoli N. Ch...
To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number ...