[37] David JC MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003. 5
[38] Roman Novak, Yasaman Bahri, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein. Sensitivity and Generalization in Neural Networks: An Empirical Study. In International Conference on Learning Representations, 2018. 1, 2, 3
[39] Kazuki Osawa, Satoki Ishikawa, Rio Yokota, Shigang Li, and Torsten Hoefler. ASDL: A Unified Interface for Gradient Preconditioning in PyTorch. In NeurIPS Workshop Order up! The Benefits of Higher-Order Optimization in Machine Learning, 2023. 8
[40] Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite. Deep Learning on a Data Diet: Finding Important Examples Early in Training. In Advances in Neural Information Processing Systems, 2021. 3, 6, 7
[41] Daryl Pregibon. Logistic Regression Diagnostics. The Annals of Statistics, 1981. 14
[42] Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan. Estimating Training Data Influence by Tracing Gradient Descent. In Advances in Neural Information Processing Systems, 2020. 3, 6
[43] Kamiar Rahnama Rad and Arian Maleki. A Scalable Estimate of the Out-of-Sample Prediction Error via Approximate Leave-One-Out Cross-Validation. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2020. 7
[44] Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In International Conference on Machine Learning, 2014. 7, 17
[45] Hugh Salimbeni, Stefanos Eleftheriadis, and James Hensman. Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. In International Conference on Artificial Intelligence and Statistics, 2018. 20
[46] Frank Schneider, Lukas Balles, and Philipp Hennig. DeepOBS: A Deep Learning Optimizer Benchmark Suite. In International Conference on Learning Representations, 2019. 21
[47] Bernhard Schölkopf, Ralf Herbrich, and Alex J Smola. A Generalized Representer Theorem. In International Conference on Computational Learning Theory, 2001. 4
[48] Saurabh Singh and Shankar Krishnan. Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020. 21
[49] Ryutaro Tanno, Melanie F Pradier, Aditya Nori, and Yingzhen Li. Repairing Neural Networks by Leaving the Right Past Behind. Advances in Neural Information Processing Systems, 2022. 5
[50] Luke Tierney and Joseph B Kadane. Accurate Approximations for Posterior Moments and Marginal Densities. Journal of the American Statistical Association, 1986. 5
[51] Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. An Empirical Study of Example Forgetting during Deep Neural Network Learning. In International Conference on Learning Representations, 2019. 3, 6
[52] Robert Weiss. An Approach to Bayesian Sensitivity Analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996. 3
[53] Fuzhao Xue, Yao Fu, Wangchunshu Zhou, Zangwei Zheng, and Yang You. To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis. ArXiv e-Prints, 2023. 3
[54] Hongtu Zhu, Joseph G. Ibrahim, Sikyum Lee, and Heping Zhang. Perturbation Selection and Influence Measures in Local Influence Analysis. The Annals of Statistics, 2007. 1