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Multi-Armed Bandits

1044 directly classified papers

Papers per year

Papers

Regret, stability & fairness in matching markets with bandit learners AISTATS 2022
Debiasing Samples from Online Learning Using Bootstrap AISTATS 2022
A Bayesian Approach for Stochastic Continuum-armed Bandit with Long-term Constraints AISTATS 2022
Hierarchical Bayesian Bandits AISTATS 2022
Nearly Optimal Algorithms for Level Set Estimation AISTATS 2022
Thompson Sampling with a Mixture Prior AISTATS 2022
Approximate Top-$m$ Arm Identification with Heterogeneous Reward Variances AISTATS 2022
Trading Off Resource Budgets For Improved Regret Bounds NIPS 2022
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits NIPS 2022
Top Two Algorithms Revisited NIPS 2022
Observation-Free Attacks on Stochastic Bandits NIPS 2021
Design of Experiments for Stochastic Contextual Linear Bandits NIPS 2021
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization NIPS 2021
Bandits with many optimal arms NIPS 2021
Fair Exploration via Axiomatic Bargaining NIPS 2021
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms NIPS 2021
Bandits with Knapsacks beyond the Worst Case NIPS 2021
Adversarial Combinatorial Bandits with General Non-linear Reward Functions ICML 2021
Auditing Black-Box Prediction Models for Data Minimization Compliance NIPS 2021
Dueling Bandits with Team Comparisons NIPS 2021
Fairness of Exposure in Stochastic Bandits ICML 2021
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism ICML 2021
Combinatorial Gaussian Process Bandits with Probabilistically Triggered Arms AISTATS 2021
Approximation Theory Based Methods for RKHS Bandits ICML 2021
Reinforcement Learning for Cost-Aware Markov Decision Processes ICML 2021