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 …

Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with
numerous practical applications. Current approaches focus on generalising existing …

In this work, we develop linear bandit algorithms that automatically adapt to different
environments. By plugging a novel loss estimator into the optimization problem that …

Adaptivity to the difficulties of a problem is a key property in sequential decision-making
problems to broaden the applicability of algorithms. Follow-the-regularized-leader (FTRL) …

We consider the development of adaptive, instance-dependent algorithms for interactive
decision making (bandits, reinforcement learning, and beyond) that, rather than only …

This study considers the partial monitoring problem with $ k $-actions and $ d $-outcomes
and provides the first best-of-both-worlds algorithms, whose regrets are favorably bounded …

Partial monitoring is a rich framework for sequential decision making under uncertainty that
generalizes many well known bandit models, including linear, combinatorial and dueling …

We prove a new minimax theorem connecting the worst-case Bayesian regret and minimax
regret under finite-action partial monitoring with no assumptions on the space of signals or …

Partial monitoring is an expressive framework for sequential decision-making with an
abundance of applications, including graph-structured and dueling bandits, dynamic pricing …

This work addresses learning online fair division under uncertainty, where a central planner
sequentially allocates items without precise knowledge of agents' values or utilities …