This paper addresses the problem of scheduling jobs in soft real-time systems, where the utility of completing each job decreases over time. We present a utility-based framework fo...
Functional verification is widely acknowledged as the bottleneck in the hardware design cycle. This paper addresses one of the main challenges of simulation based verification (or...
Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal stra...
We present a data-driven approach to learn user-adaptive referring expression generation (REG) policies for spoken dialogue systems. Referring expressions can be difficult to unde...
Adaptor grammars (Johnson et al., 2007b) are a non-parametric Bayesian extension of Probabilistic Context-Free Grammars (PCFGs) which in effect learn the probabilities of entire s...