The framework of feedback graphs is a generalization of sequential decision-making with
bandit or full information feedback. In this work, we study an extension where the directed …

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 …

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 …

We study a variant of online multiclass classification where the learner predicts a single
label but receives a\textit {set of labels} as feedback. In this model, the learner is penalized …

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

We consider prediction with expert advice for strongly convex and bounded losses, and
investigate trade-offs between regret and``variance''(ie, squared difference of learner's …

This paper studies online structured prediction with full-information feedback. For online
multiclass classification, van der Hoeven (2020) has obtained surrogate regret bounds …

We focus on the online-based active learning (OAL) setting where an agent operates over a
stream of observations and trades-off between the costly acquisition of information (labelled …

We investigate online classification with paid stochastic experts. Here, before making their
prediction, each expert must be paid. The amount that we pay each expert directly …

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