[6] Samyadeep Basu, Phil Pope, and Soheil Feizi. Influence functions in deep learning are fragile. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=xHKVVHGDOEk.
[7] Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, and Roger B Grosse. If influence functions are the answer, then what is the question? In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 17953-17967. Curran Associates, Inc., 2022. URL https://proceedings.neurips.cc/paper_files/paper/2022/file/7234e0c36fdbcb23e7bd56b68838999b-Paper-Conference.pdf.
[8] Jacob Epifano, Ravichandran Ramachandran, Aaron J. Masino, and Ghulam Rasool. Revisiting the fragility of influence functions. Neural networks : the official journal of the International Neural Network Society, 162:581-588, 2023.
[9] Karthikeyan K and Anders Søgaard. Revisiting methods for finding influential examples. CoRR, abs/2111.04683, 2021. URL https://arxiv.org/abs/2111.04683.
[10] Max Welling and Yee W Teh. Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11), pages 681–688, 2011.
[11] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017.
[12] Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pages 876-885. Association For Uncertainty in Artificial Intelligence (AUAI), 2018.
[13] Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, and Richard Zemel. Understanding the origins of bias in word embeddings. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 803-811. PMLR, 09-15 Jun 2019. URL https://proceedings.mlr.press/v97/brunet19a.html.
[14] Ekin Akyurek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, and Kelvin Guu. Towards tracing knowledge in language models back to the training data. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2429-2446, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics. URL https://aclanthology.org/2022-findings-emnlp.180.
[15] Frank R. Hampel. The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69(346):383-393, 1974. doi: 10.1080/01621459.1974.10482962.
[16] Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, and Caiming Xiong. FastIF: Scalable influence functions for efficient model interpretation and debugging. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10333-10350, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.emnlp-main.808. URL https://aclanthology.org/2021.emnlp-main.808.
[17] Andrea Schioppa, Polina Zablotskaia, David Vilar, and Artem Sokolov. Scaling up influence functions. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8):8179-8186, Jun. 2022. doi: 10.1609/aaai.v36i8.20791. URL https://ojs(aaai.org/index.php/AAAI/article/view/20791.
[18] Guillaume Charpiat, Nicolas Girard, Loris Felardos, and Yuliya Tarabalka. Input similarity from the neural network perspective. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL https://proceedings.neurips.cc/paper_files/paper/2019/file/c61f571dbd2fb949d3fe5ae1608dd48b-Paper.pdf.