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Poster
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs
Chloé Rouyer · Dirk van der Hoeven · Nicolò Cesa-Bianchi · Yevgeny Seldin

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #839
in Poster Session 6 »
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 present a computationally-efficient algorithm for learning in this framework that simultaneously achieves near-optimal regret bounds in both stochastic and adversarial environments. The bound against oblivious adversaries is $\tilde{O} (\sqrt{\alpha T})$, where $T$ is the time horizon and $\alpha$ is the independence number of the feedback graph. The bound against stochastic environments is $O\big((\ln T)^2 \max_{S\in \mathcal I(G)} \sum_{i \in S} \Delta_i^{-1}\big)$ where $\mathcal I(G)$ is the family of all independent sets in a suitably defined undirected version of the graph and $\Delta_i$ are the suboptimality gaps.The algorithm combines ideas from the EXP3++ algorithm for stochastic and adversarial bandits and the EXP3.G algorithm for feedback graphs with a novel exploration scheme. The scheme, which exploits the structure of the graph to reduce exploration, is key to obtain best-of-both-worlds guarantees with feedback graphs. We also extend our algorithm and results to a setting where the feedback graphs are allowed to change over time.
Keywords: Online Learning · feedback graphs · Beyond Bandits
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Chloé Rouyer (University of Copenhagen)
Dirk van der Hoeven (Università degli Studi di Milano)
Nicolò Cesa-Bianchi (Università degli Studi di Milano, Italy)
Yevgeny Seldin (University of Copenhagen)

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