We study a class of Markovian optimal stochastic control problems in which the controlled process Z is constrained to satisfy an a.s. constraint Z (T) G Rd+1 P - a.s. at some fi...
We study the problem of minimizing the expected loss of a linear predictor while constraining its sparsity, i.e., bounding the number of features used by the predictor. While the r...
Design space exploration during high level synthesis is often conducted through ad-hoc probing of the solution space using some scheduling algorithm. This is not only time consumi...
Gang Wang, Wenrui Gong, Brian DeRenzi, Ryan Kastne...
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...
In this paper, we propose an iteration-free algorithm to find the optimal configuration, including transmit power and source coding rates, to maximize the lifetime of a cluster ...