Certifying some distributional robustness with principled adversarial training A Sinha, H Namkoong, R Volpi, J Duchi arXiv preprint arXiv:1710.10571, 2017 | 1106 | 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 | 809 | 2018 |
Fairness without demographics in repeated loss minimization T Hashimoto, M Srivastava, H Namkoong, P Liang International Conference on Machine Learning, 1929-1938, 2018 | 611 | 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 | 606 | 2022 |
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 | 471 | 2022 |
Learning models with uniform performance via distributionally robust optimization J Duchi, H Namkoong Annals of Statistics 49 (3), 1378-1406, 2021 | 384 | 2021 |
Variance-based regularization with convex objectives J Duchi, H Namkoong Journal of Machine Learning Research 20 (68), 1-55, 2019 | 376 | 2019 |
Statistics of robust optimization: A generalized empirical likelihood approach J Duchi, P Glynn, H Namkoong Mathematics of Operations Research 46 (3), 946-969, 2021 | 359 | 2021 |
Stochastic gradient methods for distributionally robust optimization with f-divergences H Namkoong, JC Duchi Advances in neural information processing systems 29, 2016 | 342 | 2016 |
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 | 254 | 2018 |
Openclip G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... | 192 | 2021 |
Openclip, July 2021 G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... URL https://doi. org/10.5281/zenodo 5143773, 29, 0 | 185 | |
Distributionally robust losses for latent covariate mixtures J Duchi, T Hashimoto, H Namkoong Operations Research 71 (2), 649-664, 2023 | 157* | 2023 |
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 | 86* | 2022 |
Openclip, 2021 G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... If you use this software, please cite it as below 3 (5), 0 | 74 | |
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 | 68 | 2020 |
Adaptive sampling probabilities for non-smooth optimization H Namkoong, A Sinha, S Yadlowsky, JC Duchi International Conference on Machine Learning, 2574-2583, 2017 | 46 | 2017 |
Assessing External Validity Over Worst-case Subpopulations S Jeong, H Namkoong Conference on Learning Theory, 2079-2084, 2020 | 30* | 2020 |
Evaluating model performance under worst-case subpopulations M Li, H Namkoong, S Xia Advances in Neural Information Processing Systems 34, 17325-17334, 2021 | 19 | 2021 |
Diagnosing model performance under distribution shift TT Cai, H Namkoong, S Yadlowsky arXiv preprint arXiv:2303.02011, 2023 | 16 | 2023 |