Abstract. We study a model of games that combines concurrency, imperfect information and stochastic aspects. Those are finite states games in which, at each round, the two players...
Abstract. Stochastic Local Search (SLS) is a simple and effective paradigm for attacking a variety of Combinatorial (Optimization) Problems (COP). However, it is often non-trivial...
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
The main goal of this paper is to extend sPBC with the iteration operator, providing an operational semantics for the language, as well as a denotational semantics, which is based ...