In this paper we develop a theoretical analysis of the performance of sampling-based fitted value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decisi...
We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
We consider the problem of evaluating a large number of XPath expressions on an XML stream. Our main contribution consists in showing that Deterministic Finite Automata (DFA) can b...
Todd J. Green, Gerome Miklau, Makoto Onizuka, Dan ...
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...
A number of strategies have been proposed for state-based class testing. An important proposal was made by Chow [5] and adapted by Binder [3]: It consists in deriving test sequenc...
Giuliano Antoniol, Lionel C. Briand, Massimiliano ...