Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
We present a new family of proximal point methods for solving monotone variational inequalities. Our algorithm has a relative error tolerance criterion in solving the proximal subp...
Bisubmodularity extends the concept of submodularity to set functions with two arguments. We show how bisubmodular maximization leads to richer value-of-information problems, usin...
Abstract. We present a multi-pass interprocedural analysis and transformation for the functional aggregate update problem. Our solution handles untyped programs, including unrestri...
At Crypto ’06, Bellare presented new security proofs for HMAC and NMAC, under the assumption that the underlying compression function is a pseudo-random function family. Converse...