This study considers online learning with general directed feedback graphs. For this
problem, we present best-of-both-worlds algorithms that achieve nearly tight regret bounds …

We consider online learning with feedback graphs, a sequential decision-making framework
where the learner's feedback is determined by a directed graph over the action set. We …

In this work, we improve on the upper and lower bounds for the regret of online learning with
strongly observable undirected feedback graphs. The best known upper bound for this …

We study sequential bilateral trade where sellers and buyers valuations are completely
arbitrary ({\sl ie}, determined by an adversary). Sellers and buyers are strategic agents with …

A common explanation for negative user impacts of content recommender systems is
misalignment between the platform's objective and user welfare. In this work, we show that …

We study a contextual version of online combinatorial optimisation with full and semi-bandit
feedback. In this sequential decision-making problem, an online learner has to select an …

While contextual bandit has a mature theory, effectively leveraging different feedback
patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs …

Bandits with feedback graphs are powerful online learning models that interpolate between
the full information and classic bandit problems, capturing many real-life applications. A …

Sequential learning with feedback graphs is a natural extension of the multi-armed bandit
problem where the problem is equipped with an underlying graph structure that provides …

We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn online the
optimal source placement in large scale networks, such that the reward obtained from a …