We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the paramet...
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...
Although necessary, learning to discover new solutions is often long and difficult, even for supposedly simple tasks such as counting. On the other hand, learning by imitation pr...
A key problem in playing strategy games is learning how to allocate resources effectively. This can be a difficult task for machine learning when the connections between actions a...
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation trade...