This paper considers online stochastic optimization problems where time constraints severely limit the number of offline optimizations which can be performed at decision time and/...
We extended language modeling approaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our approach is based on the anal...
Variational methods have proved popular and effective for inference and learning in intractable graphical models. An attractive feature of the approaches based on the Kullback-Lei...
We extend the differential approach to inference in Bayesian networks (BNs) (Darwiche, 2000) to handle specific problems that arise in the context of dynamic Bayesian networks (D...
Parameters of statistical distributions that are input to simulations are typically not known with certainty. For existing systems, or variations on existing systems, they are oft...