| Certifying some distributional robustness with principled adversarial training A Sinha, H Namkoong, R Volpi, J Duchi arXiv preprint arXiv:1710.10571, 2017 | 1002 | 2017 |
| Generalizing to unseen domains via adversarial data augmentation R Volpi, H Namkoong, O Sener, JC Duchi, V Murino, S Savarese Advances in neural information processing systems 31, 2018 | 695 | 2018 |
| Fairness without demographics in repeated loss minimization T Hashimoto, M Srivastava, H Namkoong, P Liang International Conference on Machine Learning, 1929-1938, 2018 | 519 | 2018 |
| Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time M Wortsman, G Ilharco, SY Gadre, R Roelofs, R Gontijo-Lopes, ... International Conference on Machine Learning, 23965-23998, 2022 | 361 | 2022 |
| Variance-based regularization with convex objectives J Duchi, H Namkoong Journal of Machine Learning Research 20 (68), 1-55, 2019 | 332 | 2019 |
| Learning models with uniform performance via distributionally robust optimization J Duchi, H Namkoong Annals of Statistics 49 (3), 1378-1406, 2021 | 320 | 2021 |
| Stochastic gradient methods for distributionally robust optimization with f-divergences H Namkoong, JC Duchi Advances in neural information processing systems 29, 2016 | 300 | 2016 |
| Statistics of robust optimization: A generalized empirical likelihood approach J Duchi, P Glynn, H Namkoong Mathematics of Operations Research 46 (3), 946-969, 2021 | 295 | 2021 |
| Robust fine-tuning of zero-shot models M Wortsman, G Ilharco, JW Kim, M Li, S Kornblith, R Roelofs, RG Lopes, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 292 | 2022 |
| Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation M O'Kelly, A Sinha, H Namkoong, J Duchi, R Tedrake Advances in Neural Information Processing Systems, 2018 | 226 | 2018 |
| Distributionally robust losses for latent covariate mixtures J Duchi, T Hashimoto, H Namkoong Operations Research 71 (2), 649-664, 2023 | 136* | 2023 |
| Openclip, July 2021 G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... If you use this software, please cite it as below 2 (4), 5, 0 | 90 | |
| OpenCLIP G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... Zenodo 4, 5, 2021 | 74 | 2021 |
| Bounds on the conditional and average treatment effect with unobserved confounding factors S Yadlowsky, H Namkoong, S Basu, J Duchi, L Tian Annals of Statistics 50 (5), 2587-2615, 2022 | 71* | 2022 |
| Off-policy policy evaluation for sequential decisions under unobserved confounding H Namkoong, R Keramati, S Yadlowsky, E Brunskill Advances in Neural Information Processing Systems 33, 18819-18831, 2020 | 55 | 2020 |
| Adaptive sampling probabilities for non-smooth optimization H Namkoong, A Sinha, S Yadlowsky, JC Duchi International Conference on Machine Learning, 2574-2583, 2017 | 48 | 2017 |
| Assessing External Validity Over Worst-case Subpopulations S Jeong, H Namkoong Conference on Learning Theory, 2079-2084, 2020 | 26* | 2020 |
| Evaluating model performance under worst-case subpopulations M Li, H Namkoong, S Xia Advances in Neural Information Processing Systems 34, 17325-17334, 2021 | 12 | 2021 |
| Modeling Interference Using Experiment Roll-out A Boyarsky, H Namkoong, J Pouget-Abadie arXiv preprint arXiv:2305.10728, 2023 | 6 | 2023 |
| Distilled thompson sampling: Practical and efficient thompson sampling via imitation learning H Namkoong, S Daulton, E Bakshy arXiv preprint arXiv:2011.14266, 2020 | 6 | 2020 |
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityVerified email at stanford.edu
