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1. Corralling Stochastic Bandit Algorithms

   Arora, Raman; Vanislavov, Teodor Marinov; Mohri, Mehryar (January 2021, Proceedings of Machine Learning Research)

   We study the problem of corralling stochastic bandit algorithms, that is combining multiple bandit algorithms designed for a stochastic environment, with the goal of devising a corralling algorithm that performs almost as well as the best base algorithm. We give two general algorithms for this setting, which we show benefit from favorable regret guarantees. We show that the regret of the corralling algorithms is no worse than that of the best algorithm containing the arm with the highest reward, and depends on the gap between the highest reward and other rewards. 

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2. LLMs-augmented Contextual Bandit

   Baheri, Ali; Alm, Cecilia (November 2023, NeurIPS 2023 Foundation Models for Decision Making Workshop)
3. Combinatorial Stochastic-Greedy Bandit

   Fourati, Fares and (February 2024, AAAI)
4. Post-Contextual-Bandit Inference

   Bibaut, Aurelien; Dimakopoulou, Maria; Kallus, Nathan; Chambaz, Antoine; van der Laan, Mark (January 2021, Advances in neural information processing systems)

   Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study participants and increase the chance of identifying good or even best policies. To support credible inference on novel interventions at the end of the study, nonetheless, we still want to construct valid confidence intervals on average treatment effects, subgroup effects, or value of new policies. The adaptive nature of the data collected by contextual bandit algorithms, however, makes this difficult: standard estimators are no longer asymptotically normally distributed and classic confidence intervals fail to provide correct coverage. While this has been addressed in non-contextual settings by using stabilized estimators, variance stabilized estimators in the contextual setting pose unique challenges that we tackle for the first time in this paper. We propose the Contextual Adaptive Doubly Robust (CADR) estimator, a novel estimator for policy value that is asymptotically normal under contextual adaptive data collection. The main technical challenge in constructing CADR is designing adaptive and consistent conditional standard deviation estimators for stabilization. Extensive numerical experiments using 57 OpenML datasets demonstrate that confidence intervals based on CADR uniquely provide correct coverage. 

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5. Bandit Quickest Changepoint Detection

   Gopalan, Aditya; Lakshminarayanan, Braghadeesh; Saligrama, Venkatesh (December 2021, Advances in Neural Information Processing Systems 34 (NeurIPS 2021))