Sparsity, variance and curvature in multi-armed bandits
This work provides theoretical advances for researchers in online learning and bandit theory, solving specific open problems but is incremental in nature.
The paper tackles open problems in adversarial multi-armed and linear bandits by developing algorithms with refined regret bounds under assumptions of sparsity, variance, and curvature, achieving results such as $ ilde{O}(\sqrt{s T})$-regret for $s$-sparse losses and $ ilde{O}(\sqrt{Q})$-regret for loss variation bounded by $Q$.
In (online) learning theory the concepts of sparsity, variance and curvature are well-understood and are routinely used to obtain refined regret and generalization bounds. In this paper we further our understanding of these concepts in the more challenging limited feedback scenario. We consider the adversarial multi-armed bandit and linear bandit settings and solve several open problems pertaining to the existence of algorithms with favorable regret bounds under the following assumptions: (i) sparsity of the individual losses, (ii) small variation of the loss sequence, and (iii) curvature of the action set. Specifically we show that (i) for $s$-sparse losses one can obtain $\tilde{O}(\sqrt{s T})$-regret (solving an open problem by Kwon and Perchet), (ii) for loss sequences with variation bounded by $Q$ one can obtain $\tilde{O}(\sqrt{Q})$-regret (solving an open problem by Kale and Hazan), and (iii) for linear bandit on an $\ell_p^n$ ball one can obtain $\tilde{O}(\sqrt{n T})$-regret for $p \in [1,2]$ and one has $\tildeΩ(n \sqrt{T})$-regret for $p>2$ (solving an open problem by Bubeck, Cesa-Bianchi and Kakade). A key new insight to obtain these results is to use regularizers satisfying more refined conditions than general self-concordance