[53] Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, and Michael Mahoney. Generalization bounds using lower tail exponents in stochastic optimizers. In International Conference on Machine Learning, pages 8774-8795, 2022.
[54] Anant Raj, Lingjiong Zhu, Mert Gurbuzbalaban, and Umut Simsekli. Algorithmic stability of heavy-tailed sgd with general loss functions. In International Conference on Machine Learning, pages 28578-28597, 2023.
[55] Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, and Giulio Biroli. Comparing dynamics: Deep neural networks versus glassy systems. In International Conference on Machine Learning, pages 314-323, 2018.
[56] Jean Barbier, Florent Krzakala, Nicolas Macris, Léo Miolane, and Lenka Zdeborova. Optimal errors and phase transitions in high-dimensional generalized linear models. Proceedings of the National Academy of Sciences, 116(12):5451-5460, 2019.
[57] Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos. Beyond neural scaling laws: beating power law scaling via data pruning. In Advances in Neural Information Processing Systems, 2022.
[58] Yefan Zhou, Yaoqing Yang, Arin Chang, and Mahoney W Michael. A three-regime model of network pruning. In International Conference on Machine Learning, 2023.
[59] Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio. Fantastic generalization measures and where to find them. In International Conference on Learning Representations, 2019.
[60] Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, and Daniel M Roy. In search of robust measures of generalization. In Advances in Neural Information Processing Systems, 2020.
[61] Peter Bartlett, Dylan Foster, and Matus Telgarsky. Spectrally-normalized margin bounds for neural networks. In Advances in Neural Information Processing Systems, 2017.
[62] Huanrui Yang, Xiaoxuan Yang, Neil Zhenqiang Gong, and Yiran Chen. Hero: Hessian-enhanced robust optimization for unifying and improving generalization and quantization performance. In Proceedings of the 59th ACM/IEEE Design Automation Conference, 2022.
[63] Zhen Dong, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer. HAWQ: Hessian aware quantization of neural networks with mixed-precision. In IEEE/CVF International Conference on Computer Vision, 2019.
[64] Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer. Q-BERT: Hessian based ultra low precision quantization of bert. In AAAI Conference on Artificial Intelligence, 2020.
[65] Amartya Sanyal, Philip H. Torr, and Puneet K. Dokania. Stable rank normalization for improved generalization in neural networks and gans. In International Conference on Learning Representations, 2020.
[66] Wuyang Chen, Xinyu Gong, and Zhangyang Wang. Neural architecture search on imagenet in fourgpu hours: A theoretically inspired perspective. In International Conference on Learning Representations, 2021.
[67] David A McAllester. Some pac-bayesian theorems. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, 1998.
[68] John Langford and John Shawe-Taylor. Pac-bayes & margins. In Advances in Neural Information Processing Systems, 2002.
[69] Gintare Karolina Dziugaite and Daniel M Roy. Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data. In Annual Conference on Uncertainty in Artificial Intelligence, 2017.