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Degenerate Feedback Loops in Recommender Systems., , , , and . AIES, page 383-390. ACM, (2019)Adaptivity, Variance and Separation for Adversarial Bandits., and . CoRR, (2019)Time Consistent Discounting., and . ALT, volume 6925 of Lecture Notes in Computer Science, page 383-397. Springer, (2011)Asymptotically Optimal Agents., and . ALT, volume 6925 of Lecture Notes in Computer Science, page 368-382. Springer, (2011)Universal Knowledge-Seeking Agents for Stochastic Environments., , and . ALT, volume 8139 of Lecture Notes in Computer Science, page 158-172. Springer, (2013)Exploration by Optimisation in Partial Monitoring., and . COLT, volume 125 of Proceedings of Machine Learning Research, page 2488-2515. PMLR, (2020)Free Lunch for optimisation under the universal distribution., , and . IEEE Congress on Evolutionary Computation, page 167-174. IEEE, (2014)Matrix games with bandit feedback., , and . UAI, volume 161 of Proceedings of Machine Learning Research, page 279-289. AUAI Press, (2021)Refined Lower Bounds for Adversarial Bandits., and . NIPS, page 1190-1198. (2016)Regret Bounds for Information-Directed Reinforcement Learning., and . NeurIPS, (2022)