Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
In this paper we look at combining and compressing a set of workflows, such that computation can be minimized. In this context, we look at two novel theoretical problems with appl...
Dhrubajyoti Saha, Abhishek Samanta, Smruti R. Sara...
—In this paper we consider an interacting two-agent sequential decision-making problem consisting of a Markov source process, a causal encoder with feedback, and a causal decoder...
This paper presents a rapid voice adaptation algorithm using GMM-based frequency warping and shift with parameters of a subband basis spectrum model (SBM)[1]. The SBM parameter re...
Multiple sequence alignment is a central problem in Bioinformatics. A known integer programming approach is to apply branch-and-cut to exponentially large graph-theoretic models. T...
Steven David Prestwich, Desmond G. Higgins, Orla O...