Refined Lower Bounds for Adversarial Bandits

NeurIPS 2016 Sébastien GerchinovitzTor Lattimore

We provide new lower bounds on the regret that must be suffered by adversarial bandit algorithms. The new results show that recent upper bounds that either (a) hold with high-probability or (b) depend on the total lossof the best arm or (c) depend on the quadratic variation of the losses, are close to tight... (read more)

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