Sicco Verwer

Learning Deterministic Finite Automata from Infinite Alphabets

2016 · Open Repository and Bibliography (University of Luxembourg)

We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals over the alphabet. …

Interpreting Finite Automata for Sequential Data

2016 · arXiv (Cornell University)

Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specifying objectives or desired attributes. In this paper, we identify the key …

Results of the Sequence PredIction ChallengE (SPiCe): a Competition on Learning the Next Symbol in a Sequence

2016 · HAL (Le Centre pour la Communication Scientifique Directe)

International audience

Learning Optimal Classification Trees Using a Binary Linear Program Formulation

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

We provide a new formulation for the problem of learning the optimal classification tree of a given depth as a binary linear program. A limitation of previously proposed Mathematical Optimization formulations is that they create …