| Performative prediction in a stateful world G Brown, S Hod, I Kalemaj International Conference on Artificial Intelligence and Statistics, 6045-6061, 2022 | 70 | 2022 |
| Clusterability in Neural Networks D Filan, S Casper, S Hod, C Wild, A Critch, S Russell arXiv preprint arXiv:2103.03386, 2021 | 30 | 2021 |
| Pruned neural networks are surprisingly modular D Filan, S Hod, C Wild, A Critch, S Russell arXiv e-prints, arXiv: 2003.04881, 2020 | 19 | 2020 |
| Detecting modularity in deep neural networks S Hod, S Casper, D Filan, C Wild, A Critch, S Russell | 11* | 2021 |
| Data science meets law S Hod, K Chagal-Feferkorn, N Elkin-Koren, A Gal Communications of the ACM 65 (2), 35-39, 2022 | 10 | 2022 |
| Graphical clusterability and local specialization in deep neural networks S Casper, S Hod, D Filan, C Wild, A Critch, S Russell ICLR 2022 Workshop on PAIR {\textasciicircum} 2Struct: Privacy …, 2022 | 9 | 2022 |
| Quantifying local specialization in deep neural networks S Hod, D Filan, S Casper, A Critch, S Russell arXiv preprint arXiv:2110.08058, 2021 | 9 | 2021 |
| Responsibly: Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems S Hod | 7 | 2018 |
| Differentially Private Release of Israel's National Registry of Live Births S Hod, R Canetti arXiv preprint arXiv:2405.00267, 2024 | 4 | 2024 |
| ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries R Cummings, S Hod, J Sarathy, M Swanberg arXiv preprint arXiv:2405.01716, 2024 | | 2024 |
| Comment on “NIST SP 800-226: Guidelines for Evaluating Differential Privacy Guarantees” R Cummings, S Hod, G Kaptchuk, P Nanayakkara, J Sarathy, J Seeman | | 2024 |
| Designing the Pilot Release of Israel's National Registry of Live Births: Reconciling Privacy with Accuracy and Usability S Hod | | 2023 |
| Increasing fairness in targeted advertising N Birner, S Hod, MC Kettemann, A Pirang, F Stock, E Eren, L Hondrich, ... | | |
Daniel FilanPhD Student, UC Berkeley在 berkeley.edu 的电子邮件经过验证
