Successful application of reinforcement learning algorithms often involves considerable hand-crafting of the necessary non-linear features to reduce the complexity of the value fu...
We introduce the Concave-Convex procedure (CCCP) which constructs discrete time iterative dynamical systems which are guaranteed to monotonically decrease global optimization/ener...
Calvin and Nakayama previously introduced permuting as a way of improving existing standardized time series methods. The basic idea is to split a simulated sample path into nonove...
We review the basic principles of Quasi-Monte Carlo (QMC) methods, the randomizations that turn them into variancereduction techniques, and the main classes of constructions under...
Multi-agent models of language evolution usually involve agents giving names to internal independently constructed categories. We present an approach in which the creation of cate...