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Regret Bounds for Generalized Linear Bandits under Parameter Drift., , , and . CoRR, (2021)A/B/n Testing with Control in the Presence of Subpopulations., , , , , and . NeurIPS, page 25100-25110. (2021)A Technical Note on Non-Stationary Parametric Bandits: Existing Mistakes and Preliminary Solutions., , , and . ALT, volume 132 of Proceedings of Machine Learning Research, page 619-626. PMLR, (2021)Self-Concordant Analysis of Generalized Linear Bandits with Forgetting., , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 658-666. PMLR, (2021)On Limited-Memory Subsampling Strategies for Bandits., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 727-737. PMLR, (2021)Weighted Linear Bandits for Non-Stationary Environments., , and . NeurIPS, page 12017-12026. (2019)Efficient Algorithms for Extreme Bandits., , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 2210-2248. PMLR, (2022)Embeddings of Categorical Variables for Sequential Data in Fraud Context., , and . AMLTA, volume 723 of Advances in Intelligent Systems and Computing, page 542-552. Springer, (2018)Sequential decision problems in non-stationary environments. (Problèmes de décision séquentielle dans des environnements non-stationnaires).. PSL University, Paris, France, (2022)Self-Concordant Analysis of Generalized Linear Bandits with Forgetting., , , and . CoRR, (2020)