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Best-of-All-Worlds Bounds for Online Learning with Feedback Graphs
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Abstract: We study the online learning with feedback graphs framework introduced by
Mannor and Shamir (2011), in which the feedback received by the online learner
is specified by a graph $G$ over the available actions. We develop an algorithm
that simultaneously achieves regret bounds of the form:
$\smash{\mathcal{O}(\sqrt{\theta(G) T})}$ with adversarial losses;
$\mathcal{O}(\theta(G)\operatorname{polylog}{T})$ with stochastic losses; and
$\mathcal{O}(\theta(G)\operatorname{polylog}{T} + \smash{\sqrt{\theta(G) C ...
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Interpretation of convexity lemma
Best-of-All-Worlds Bounds for Online Learning with Feedback Graphs
Best-of-All-Worlds Bounds for Online Learning with Feedback Graphs
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