Hongseok Namkoong
Hongseok Namkoong
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Geciteerd door
Certifiable distributional robustness with principled adversarial training
A Sinha, H Namkoong, J Duchi
arXiv preprint arXiv:1710.10571 2, 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
Fairness without demographics in repeated loss minimization
T Hashimoto, M Srivastava, H Namkoong, P Liang
International Conference on Machine Learning, 1929-1938, 2018
Variance-based regularization with convex objectives
H Namkoong, JC Duchi
Advances in neural information processing systems 30, 2017
Stochastic gradient methods for distributionally robust optimization with f-divergences
H Namkoong, JC Duchi
Advances in neural information processing systems 29, 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
Learning models with uniform performance via distributionally robust optimization
J Duchi, H Namkoong
Annals of Statistics 49 (3), 1378-1406, 2021
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
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
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
Distributionally robust losses for latent covariate mixtures
J Duchi, T Hashimoto, H Namkoong
Operations Research 71 (2), 649-664, 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
Openclip, July 2021
G Ilharco, M Wortsman, N Carlini, R Taori, A Dave, V Shankar, ...
If you use this software, please cite it as below 5, 0
Adaptive sampling probabilities for non-smooth optimization
H Namkoong, A Sinha, S Yadlowsky, JC Duchi
International Conference on Machine Learning, 2574-2583, 2017
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
Assessing External Validity Over Worst-case Subpopulations
S Jeong, H Namkoong
Conference on Learning Theory, 2079-2084, 2020
Evaluating model performance under worst-case subpopulations
M Li, H Namkoong, S Xia
Advances in Neural Information Processing Systems 34, 17325-17334, 2021
Distilled thompson sampling: Practical and efficient thompson sampling via imitation learning
H Namkoong, S Daulton, E Bakshy
arXiv preprint arXiv:2011.14266, 2020
An Operational Perspective to Fairness Interventions: Where and How to Intervene
B Hsu, X Chen, Y Han, H Namkoong, K Basu
arXiv preprint arXiv:2302.01574, 2023
Reliable Machine Learning Via Distributional Robustness
H Namkoong
Stanford University, 2019
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