linear bandit problem
On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice
This paper considers problems of multi-armed bandits with expert advice (MwE) [Auer et al.,
TightFirst-andSecond-OrderRegretBounds forAdversarialLinearBandits
In addition, we need only assumptions weaker than those of existing algorithms; our algorithms work on discrete action sets as well as continuous ones without apriori knowledge about losses, and theyrun efficiently ifalinear optimization oracle for the action set is available.
On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice
This paper considers problems of multi-armed bandits with expert advice (MwE) [Auer et al.,
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures
We present improved algorithms with worst-case regret guarantees for the stochastic linear bandit problem. The widely used "optimism in the face of uncertainty"
We thank the reviewer for the positive comments and encouraging us to point out the novelty in our
We thank the reviewers for providing detailed and quality reviews. A final novelty in our analysis is obtaining tighter bounds on the problem-dependent terms. We appreciate you pointing out several typos that we are fixing. Thanks for the detailed comments on the proof of Theorem 1. X and the index j is with respect to the m elements in Z . Thank you for the careful review and giving several useful suggestions.
Tight First-and Second-Order Regret Bounds for Adversarial Linear Bandits
We propose novel algorithms with first-and second-order regret bounds for adversarial linear bandits.