[1] Aad W. van der Vaart and Jon A. Wellner. Weak Convergence and Empirical Processes. Springer, 1996.
[2] Evarist Giné and Richard Nickl. Mathematical Foundations of Infinite-Dimensional Statistical Models. Cambridge University Press, 2016.
[3] Michel Talagrand. Upper and Lower Bounds for Stochastic Processes: Modern Methods and Classical Problems. Springer, 2014.
[4] David A. McAllester. PAC-Bayesian model averaging. In Conference on Learning Theory, 1999.
[5] Tong Zhang. Information-theoretic upper and lower bounds for statistical estimation. IEEE Transactions on Information Theory, 52(4):1307-1321, April 2006.
[6] Daniel Russo and James Zou. Controlling Bias in Adaptive Data Analysis Using Information Theory. In Conference on Artificial Intelligence and Statistics, 2016.
[7] Aolin Xu and Maxim Raginsky. Information-theoretic analysis of generalization capability of learning algorithms. In Advances in Neural Information Processing Systems, 2017.
[8] Yuheng Bu, Shaofeng Zou, and Venugopal V. Veeravalli. Tightening Mutual Information Based Bounds on Generalization Error. IEEE Journal on Selected Areas in Information Theory, 1(1):121-130, May 2020.
[9] Fredrik Hellström and Giuseppe Durisi. Generalization Bounds via Information Density and Conditional Information Density. IEEE Journal on Selected Areas in Information Theory, 1(3):824-839, November 2020.
[10] Thomas Steinke and Lydia Zakynthinou. Reasoning About Generalization via Conditional Mutual Information. arXiv:2001.09122, June 2020.
[11] Hassan Hafez-Kolahi, Zeinab Golgooni, Shohreh Kasaei, and Mahdieh Soleymani. Conditioning and processing: Techniques to improve information-theoretic generalization bounds. In Advances in Neural Information Processing Systems, 2020.
[12] Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M Roy, and Gintare Karolina Dziugaite. Sharpened generalization bounds based on conditional mutual information and an application to noisy, iterative algorithms. In Advances in Neural Information Processing Systems, 2020.
[13] Amedeo Roberto Esposito, Michael Gastpar, and Ibrahim Issa. Generalization error bounds via Rényi-,  $f$ -divergences and maximal leakage. IEEE Transactions on Information Theory, 67(8):4986-5004, August 2021.
[14] Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, and Dan Roy. Towards a unified information-theoretic framework for generalization. In Advances in Neural Information Processing Systems, 2021.
[15] Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, and Aram Galstyan. Information-theoretic generalization bounds for black-box learning algorithms. In Advances in Neural Information Processing Systems, 2021.
[16] Ruida Zhou, Chao Tian, and Tie Liu. Individually conditional individual mutual information bound on generalization error. IEEE Transactions on Information Theory, 68(5):3304-3316, May 2022.
[17] Jean-Yves Audibert and Olivier Bousquet. Combining PAC-Bayesian and Generic Chaining Bounds. Journal of Machine Learning Research, 8(32):863-889, 2007.
[18] Amir Asadi, Emmanuel Abbe, and Sergio Verdu. Chaining Mutual Information and Tightening Generalization Bounds. In Advances in Neural Information Processing Systems, 2018.
[19] Eugenio Clerico, Amitis Shidani, George Deligiannidis, and Arnaud Doucet. Chained generalisation bounds. In Conference on Learning Theory, 2022.
[20] Ruida Zhou, Chao Tian, and Tie Liu. Stochastic Chaining and Strengthened Information-Theoretic Generalization Bounds. In IEEE International Symposium on Information Theory, January 2022.