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Hongseok Namkoong
Hongseok Namkoong
Columbia University
Zweryfikowany adres z columbia.edu - Strona główna
Tytuł
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Cytowane przez
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Certifying some distributional robustness with principled adversarial training
A Sinha, H Namkoong, R Volpi, J Duchi
arXiv preprint arXiv:1710.10571, 2017
9962017
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
6882018
Fairness without demographics in repeated loss minimization
T Hashimoto, M Srivastava, H Namkoong, P Liang
International Conference on Machine Learning, 1929-1938, 2018
5132018
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
3542022
Variance-based regularization with convex objectives
H Namkoong, JC Duchi
Advances in neural information processing systems 30, 2017
327*2017
Learning models with uniform performance via distributionally robust optimization
J Duchi, H Namkoong
Annals of Statistics 49 (3), 1378-1406, 2021
3142021
Stochastic gradient methods for distributionally robust optimization with f-divergences
H Namkoong, JC Duchi
Advances in neural information processing systems 29, 2016
2962016
Statistics of robust optimization: A generalized empirical likelihood approach
J Duchi, P Glynn, H Namkoong
Mathematics of Operations Research 46 (3), 946-969, 2021
2912021
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
2852022
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
2232018
Distributionally robust losses for latent covariate mixtures
J Duchi, T Hashimoto, H Namkoong
Operations Research 71 (2), 649-664, 2023
134*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
88
OpenCLIP
G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ...
Zenodo 4, 5, 2021
722021
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
552020
Adaptive sampling probabilities for non-smooth optimization
H Namkoong, A Sinha, S Yadlowsky, JC Duchi
International Conference on Machine Learning, 2574-2583, 2017
482017
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
122021
Modeling Interference Using Experiment Roll-out
A Boyarsky, H Namkoong, J Pouget-Abadie
arXiv preprint arXiv:2305.10728, 2023
62023
Distilled thompson sampling: Practical and efficient thompson sampling via imitation learning
H Namkoong, S Daulton, E Bakshy
arXiv preprint arXiv:2011.14266, 2020
62020
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