Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

This important text and reference for researchers and students in machine learning, game
theory, statistics and information theory offers a comprehensive treatment of the problem of …

Many problems, such as cognitive radio, parameter control of a scanning tunnelling
microscope or internet advertisement, can be modelled as non-stationary bandit problems …

We study a partial-information online-learning problem where actions are restricted to noisy
comparisons between pairs of strategies (also known as bandits). In contrast to conventional …

External regret compares the performance of an online algorithm, selecting among N
actions, to the performance of the best of those actions in hindsight. Internal regret compares …

Multi-armed bandit problems are considered as a paradigm of the trade-off between
exploring the environment to find profitable actions and exploiting what is already known. In …

Many situations involve repeatedly making decisions in an uncertain envi-ronment: for
instance, deciding what route to drive to work each day, or repeated play of a game against …

Converging evidence suggest that the medial prefrontal cortex (MPFC) is involved in
feedback categorization, performance monitoring, and task monitoring, and may contribute …

This work studies external regret in sequential prediction games with both positive and
negative payoffs. External regret measures the difference between the payoff obtained by …

Bilateral trade models the problem of intermediating between two rational agents—a seller
and a buyer—both characterized by a private valuation for an item they want to trade. We …