Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement l...
Combinatorial optimization problems expressed as Boolean constraint satisfaction problems (BCSPs) arise in several contexts, ranging from the classical unate set-packing problems ...
Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization ...
Protocol composition frameworks provide off-the-shelf composable protocols to simplify the development of custom protocol stacks. All recent protocol frameworks use a general-purp...
L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm. In this paper we compare state...