[32] Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, and Robert D. Nowak. Bilinear bandits with low-rank structure. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 3163-3172. PMLR, 2019. (Cited on pages 1 and 9.)
[33] Daniel Kane, Sihan Liu, Shachar Lovett, and Gaurav Mahajan. Computational-statistical gap in reinforcement learning. In Proceedings of Thirty Fifth Conference on Learning Theory, volume 178 of Proceedings of Machine Learning Research, pages 1282-1302. PMLR, 02-05 Jul 2022. (Cited on page 10.)
[34] Yue Kang, Cho-Jui Hsieh, and Thomas Chun Man Lee. Efficient frameworks for generalized low-rank matrix bandit problems. Advances in Neural Information Processing Systems, 35:19971-19983, 2022. (Cited on pages 9 and 50.)
[35] Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, Claire Vernade, and Zheng Wen. Stochastic Rank-1 Bandits. In Aarti Singh and Jerry Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 392–401. PMLR, 20–22 Apr 2017. (Cited on page 9.)
[36] Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, and Michal Valko. Adaptive Reward-Free Exploration. In Proceedings of the 32nd International Conference on Algorithmic Learning Theory, volume 132 of Proceedings of Machine Learning Research, pages 865-891. PMLR, 16-19 Mar 2021. (Cited on page 8.)
[37] Branislav Kveton, Csaba Szepesvari, Anup Rao, Zheng Wen, Yasin Abbasi-Yadkori, and S. Muthukrishnan. Stochastic low-rank bandits, 2017. (Cited on pages 1 and 9.)
[38] Michael Laskin, Aravind Srinivas, and Pieter Abbeel. CURL: Contrastive Unsupervised Representations for Reinforcement Learning. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 5639-5650. PMLR, 13-18 Jul 2020. (Cited on page 1.)
[39] Lihua Lei. Unified  $\ell_{2\to \infty}$  eigenspace perturbation theory for symmetric random matrices. arXiv preprint arXiv:1909.04798, 2019. (Cited on page 9.)
[40] Xudong Li, Mengdi Wang, and Anru Zhang. Estimation of markov chain via rank-constrained likelihood. In International Conference on Machine Learning, pages 3033-3042. PMLR, 2018. (Cited on page 9.)
[41] Yangyi Lu, Amirhossein Meisami, and Ambuj Tewari. Low-rank generalized linear bandit problems. In Arindam Banerjee and Kenji Fukumizu, editors, The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event, volume 130 of Proceedings of Machine Learning Research, pages 460-468. PMLR, 2021. (Cited on page 9.)
[42] Andrew D McRae and Mark A Davenport. Low-rank matrix completion and denoising under poisson noise. Information and Inference: A Journal of the IMA, 10(2):697-720, 2021. (Cited on page 31.)
[43] Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, and Michal Valko. Fast active learning for pure exploration in reinforcement learning. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 7599-7608. PMLR, 18-24 Jul 2021. (Cited on page 8.)
[44] Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, and John Langford. Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 6961-6971. PMLR, 13-18 Jul 2020. (Cited on page 10.)
[45] Michael Mitzenmacher and Eli Upfal. Probability and computing: Randomization and probabilistic techniques in algorithms and data analysis. Cambridge university press, 2017. (Cited on page 22.)