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A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits., , and . ICDM (Workshops), page 272-280. IEEE, (2023)One-Step Distributional Reinforcement Learning., , , , and . Trans. Mach. Learn. Res., (2023)Weighted Emprirical Risk Minimization: Transfer Learning based on Importance Sampling., , , and . ESANN, page 515-520. (2020)Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach., , and . ALT, volume 98 of Proceedings of Machine Learning Research, page 64-93. PMLR, (2019)Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study., , , , and . CoRR, (2023)Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling., , , and . CoRR, (2020)Max K-Armed Bandit: On the ExtremeHunter Algorithm and Beyond., , , , and . ECML/PKDD (2), volume 10535 of Lecture Notes in Computer Science, page 389-404. Springer, (2017)Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study., , , , , and . ICC Workshops, page 1956-1961. IEEE, (2024)Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach., , and . CoRR, (2018)Profitable Bandits., , and . ACML, volume 95 of Proceedings of Machine Learning Research, page 694-709. PMLR, (2018)