Alexandre Lacasse, François Laviolette, Mario Marchand, and Francis Turgeon-Boutin. Learning with randomized majority votes. In José L. Balcazar, Francesco Bonchi, Aristides Gionis, and Michèle Sebag, editors, Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II, volume 6322 of Lecture Notes in Computer Science, pages 162-177. Springer, 2010. doi: 10.1007/978-3-642-15883-4\_11. URL https://doi.org/10.1007/978-3-642-15883-4_11. (Cited on page 4.)
Justin Lee. U-statistics: Theory and Practice. CiteSeer, 1990. (Cited on page 35.)
Guy Lever, François Laviolette, and John Shawe-Taylor. Tighter PAC-Bayes bounds through distribution-dependent priors. Theoretical Computer Science, 473:4-28, February 2013. ISSN 0304-3975. doi: 10.1016/j.tcs.2012.10.013. URL https://linkinghub.elsevier.com/ retrieve/pii/S0304397512009346. (Cited on page 4.)
Tong Li and Ming Yuan. On the optimality of gaussian kernel based nonparametric tests against smooth alternatives. arXiv preprint arXiv:1909.03302, 2019. (Cited on page 5.)
Yazhe Li, Roman Pogodin, Danica J. Sutherland, and Arthur Gretton. Self-supervised learning with kernel dependence maximization. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 15543-15556, 2021. URL https://proceedings.neurips.cc/paper/2021/bit/83004190b1793d7aa15f8d0d49a13eba-Abstract.html. (Cited on page 2.)
Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, and Hidetoshi Shimodaira. More powerful selective kernel tests for feature selection. In International Conference on Artificial Intelligence and Statistics, pages 820-830. PMLR, 2020. (Cited on page 5.)
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, and Dougal J Sutherland. Learning deep kernels for non-parametric two-sample tests. In International Conference on Machine Learning, 2020. (Cited on pages 2, 5, 8, 9, 18, and 21.)
Feng Liu, Wenkai Xu, Jie Lu, and Danica J. Sutherland. Meta two-sample testing: Learning kernels for testing with limited data. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 5848-5860, 2021. URL https://proceedings.neurips.cc/paper/2021/hash/2e6d9c6052e99fcdfa61d9b9da273ca2-AAbstract.html. (Cited on page 2.)
Qiang Liu, Jason Lee, and Michael Jordan. A kernelized Stein discrepancy for goodness-of-fit tests. In International Conference on Machine Learning, pages 276-284. PMLR, 2016. (Cited on page 10.)
David Lopez-Paz and Maxime Aquab. Revisiting classifier two-sample tests. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017. URL https://openreview.net/forum?id= SJkXfE5xx. (Cited on page 9.)
Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, and Yevgeny Seldin. Second order PAC-Bayesian bounds for the weighted majority vote. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/hash/386854131f58a556343e056f03626e00-AAbstract.html. (Cited on page 4.)
P. Massart. The Tight Constant in the Dvoretzky-Kiefer-Wolfowitz Inequality. The Annals of Probability, 18(3):1269 - 1283, 1990. doi: 10.1214/aop/1176990746. URL https://doi.org/10.1214/aop/1176990746. (Cited on page 34.)
Andreas Maurer. A note on the PAC-Bayesian theorem. CoRR, cs.LG/0411099, 2004. URL http://arxiv.org/abs/cs.LG/0411099. (Cited on page 4.)
David A McAllester. Some PAC-Bayesian theorems. In Proceedings of the eleventh annual conference on Computational Learning Theory, pages 230-234. ACM, 1998. (Cited on page 4.)