Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling com...
Standard Reinforcement Learning (RL) aims to optimize decision-making rules in terms of the expected return. However, especially for risk-management purposes, other criteria such ...
In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters effic...
The acquisition of novel phonetic categories is hypothesized to be affected by the distributional properties of the input, the relation of the new categories to the native phonolo...
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...