We present an on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems. In particular, ...
Abstract. We present a new exploration term, more efficient than classical UCT-like exploration terms. It combines efficiently expert rules, patterns extracted from datasets, All-M...
We consider the problem of revenue-optimal dynamic mechanism design in settings where agents' types evolve over time as a function of their (both public and private) experien...
We investigate code trees and search trees with cost functions that increase exponentially with the depth in the tree. While corresponding coding theorems have been considered in ...
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...