First Meeting
Presenter
References
- Giordano, Ryan, William Stephenson, Runjing Liu, Michael Jordan, and Tamara Broderick. “A Swiss Army Infinitesimal Jackknife.” In Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
- Koh, Pang Wei, and Percy Liang. “Understanding Black-Box Predictions via Influence Functions.” In Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.
- Agarwal, Naman, Brian Bullins, and Elad Hazan. “Second-Order Stochastic Optimization for Machine Learning in Linear Time.” Journal of Machine Learning Research (JMLR), 2017.
- Schioppa, Andrea, Polina Zablotskaia, David Vilar, and Artem Sokolov. “Scaling up Influence Functions.” In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2022.
- Martens, James. “New Insights and Perspectives on the Natural Gradient Method.” Journal of Machine Learning Research (JMLR), 2020.
- Grosse, Roger, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner et al. “Studying large language model generalization with influence functions.” arXiv preprint arXiv:2308.03296 (2023).
- George, Thomas, César Laurent, Xavier Bouthillier, Nicolas Ballas, and Pascal Vincent. 2018. “Fast Approximate Natural Gradient Descent in a Kronecker-Factored Eigenbasis.” In Proceedings of 31st Neural Information Processing Systems (NeurIPS), 2018.
- Kwon, Yongchan, Eric Wu, Kevin Wu, and James Zou. “DataInf: Efficiently Estimating Data Influence in LoRA-Tuned LLMs and Diffusion Models.” In the 12th International Conference on Learning Representations (ICLR), 2023.