arXiv Machine Learning
Tight Generalization Bounds for Noiseless Inverse Optimization
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A Computationally Efficient and Rate-Optimal Algorithm for Adversarial Linear MDPs with (Aggregate) Bandit Feedback
arXiv:2610. 30801v1 Announce Type: new Abstract: We study episodic linear Markov decision processes with unknown transitions, adversarial losses, and bandit feedback.
arXiv Machine Learning
Jun 19
Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses
arXiv:2606. 19891v1 Announce Type: new Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth.
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Breaking the Total Variance Barrier: Sharp Sample Complexity for Linear Heteroscedastic Bandits with Fixed Action Set
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Optimal Regret for Single Index Bandits
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A Perturbation Approach to Unconstrained Linear Bandits
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Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem
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