Abstract. We present a new derivative-free algorithm, ORBIT, for unconstrained local optimization of computationally expensive functions. A trust-region framework using interpolati...
Stefan M. Wild, Rommel G. Regis, Christine A. Shoe...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
We develop and evaluate a semiparametric method to estimate the mean-value function of a nonhomogeneous Poisson process (NHPP) using one or more process realizations observed over...
We treat the text summarization problem as maximizing a submodular function under a budget constraint. We show, both theoretically and empirically, a modified greedy algorithm can...
Abstract We present an efficient method for importance sampling the product of multiple functions. Our algorithm computes a quick approximation of the product on-the-fly, based on ...