SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[27] Yingkai Li, Edmund Y Lou, and Liren Shan. Stochastic linear optimization with adversarial corruption. arXiv preprint arXiv:1909.02109, 2019.
[28] Shiyin Lu, Guanghui Wang, Yao Hu, and Lijun Zhang. Optimal algorithms for lipschitz bandits with heavy-tailed rewards. In International Conference on Machine Learning, pages 4154-4163. PMLR, 2019.
[29] Thodoris Lykouris, Vahab Mirrokni, and Renato Paes Leme. Stochastic bandits robust to adversarial corruptions. In Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, pages 114–122, 2018.
[30] Stefan Magureanu, Richard Combes, and Alexandre Proutiere. Lipschitz bandits: Regret lower bound and optimal algorithms. In Conference on Learning Theory, pages 975-999. PMLR, 2014.
[31] Aldo Pacchiano, My Phan, Yasin Abbasi Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, and Csaba Szepesvari. Model selection in contextual stochastic bandit problems. Advances in Neural Information Processing Systems, 33:10328-10337, 2020.
[32] Chara Podimata and Alex Slivkins. Adaptive discretization for adversarial lipschitz bandits. In Conference on Learning Theory, pages 3788-3805. PMLR, 2021.
[33] Aleksandris Slivkins. Contextual bandits with similarity information. In Proceedings of the 24th annual Conference On Learning Theory, pages 679-702. JMLR Workshop and Conference Proceedings, 2011.
[34] Aleksandrs Slivkins. Multi-armed bandits on implicit metric spaces. Advances in Neural Information Processing Systems, 24, 2011.
[35] Aleksandrs Slivkins et al. Introduction to multi-armed bandits. Foundations and Trends® in Machine Learning, 12(1-2):1-286, 2019.
[36] Yitzchak Solomon, Alexander Wagner, and Paul Bendich. A fast and robust method for global topological functional optimization. In International Conference on Artificial Intelligence and Statistics, pages 109-117. PMLR, 2021.
[37] Chen-Yu Wei, Christoph Dann, and Julian Zimmert. A model selection approach for corruption robust reinforcement learning. In International Conference on Algorithmic Learning Theory, pages 1043-1096. PMLR, 2022.
[38] Chenlu Ye, Wei Xiong, Quanquan Gu, and Tong Zhang. Corruption-robust algorithms with uncertainty weighting for nonlinear contextual bandits and markov decision processes. In International Conference on Machine Learning, pages 39834–39863. PMLR, 2023.
[39] Heyang Zhao, Dongruo Zhou, and Quanquan Gu. Linear contextual bandits with adversarial corruptions. arXiv preprint arXiv:2110.12615, 2021.