Abstract. Most of multi-agent reinforcement learning algorithms aim to converge to a Nash equilibrium, but a Nash equilibrium does not necessarily mean a desirable result. On the o...
This paper highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders. In particu...
Arthur Guez, Robert D. Vincent, Massimo Avoli, Joe...
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant...
This paper describes a pattern classification approach for detecting frontal-view faces via learning a decision boundary. The classification can be achieved either by explicit est...