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Optimal Exploration is no harder than Thompson Sampling.

, , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 1684-1692. PMLR, (2024)

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Finite Sample Prediction and Recovery Bounds for Ordinal Embedding., , and . NIPS, page 2703-2711. (2016)Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice., , , , and . WWW, page 3586-3597. ACM, (2024)Pessimistic Off-Policy Multi-Objective Optimization., , , , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 2980-2988. PMLR, (2024)A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity., , , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 1585-1593. PMLR, (2024)Optimal Exploration is no harder than Thompson Sampling., , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 1684-1692. PMLR, (2024)Deep Learning for Military Image Captioning., , and . FUSION, page 2165-2171. IEEE, (2018)Optimal Exploration is no harder than Thompson Sampling., , and . CoRR, (2023)Learning Low-Dimensional Metrics., , and . NIPS, page 4139-4147. (2017)Active Learning with Safety Constraints., , , , and . NeurIPS, (2022)Sequential Experimental Design for Transductive Linear Bandits., , , and . NeurIPS, page 10666-10676. (2019)