Hongseok Namkoong

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Referințe bibliografice	7694	7219
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John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityAdresă de e-mail confirmată pe stanford.edu
Ludwig SchmidtStanford University and AnthropicAdresă de e-mail confirmată pe stanford.edu
Steve YadlowskyResearch Scientist, Google DeepMindAdresă de e-mail confirmată pe google.com
Tatsunori HashimotoAssistant Professor, StanfordAdresă de e-mail confirmată pe stanford.edu
Peter W. GlynnProfessor of Management Science and EngineeringAdresă de e-mail confirmată pe stanford.edu
Mike LiColumbia UniversityAdresă de e-mail confirmată pe columbia.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityAdresă de e-mail confirmată pe cs.stanford.edu
Jong Wook KimOpenAIAdresă de e-mail confirmată pe openai.com
Tiffany CaiStatistics PhD student, Columbia UniversityAdresă de e-mail confirmată pe columbia.edu
Ethan W. CheColumbia UniversityAdresă de e-mail confirmată pe columbia.edu
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityAdresă de e-mail confirmată pe cs.stanford.edu
Jiashuo LiuTsinghua UniversityAdresă de e-mail confirmată pe mails.tsinghua.edu.cn
Andrew SiahColumbia UniversityAdresă de e-mail confirmată pe columbia.edu
Naimeng YePh.D. Student, Columbia UniversityAdresă de e-mail confirmată pe columbia.edu
Tianyu WangColumbia UniversityAdresă de e-mail confirmată pe columbia.edu
Yuanzhe MaColumbia UniversityAdresă de e-mail confirmată pe columbia.edu
Ari BoyarskyColumbia UniversityAdresă de e-mail confirmată pe columbia.edu
Jean Pouget-AbadieGoogle ResearchAdresă de e-mail confirmată pe google.com
Eytan BakshyResearch Director, MetaAdresă de e-mail confirmată pe fb.com
Jing DongColumbia UniversityAdresă de e-mail confirmată pe columbia.edu

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Hongseok Namkoong

Columbia University

Adresă de e-mail confirmată pe columbia.edu - Pagina de pornire

Operations Research Machine Learning


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Certifying some distributional robustness with principled adversarial training A Sinha, H Namkoong, R Volpi, J Duchi arXiv preprint arXiv:1710.10571, 2017	1201	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	935	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	930	2022
Fairness without demographics in repeated loss minimization T Hashimoto, M Srivastava, H Namkoong, P Liang International Conference on Machine Learning, 1929-1938, 2018	715	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	683	2022
Learning models with uniform performance via distributionally robust optimization J Duchi, H Namkoong Annals of Statistics 49 (3), 1378-1406, 2021	467	2021
Variance-based regularization with convex objectives J Duchi, H Namkoong Journal of Machine Learning Research 20 (68), 1-55, 2019	420	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	407	2021
Stochastic gradient methods for distributionally robust optimization with f-divergences H Namkoong, JC Duchi Advances in neural information processing systems 29, 2016	386	2016
Openclip G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ...	300	2021
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	288	2018
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	265
Distributionally robust losses for latent covariate mixtures J Duchi, T Hashimoto, H Namkoong Operations Research 71 (2), 649-664, 2023	176*	2023
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	126
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	102*	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	80	2020
Adaptive sampling probabilities for non-smooth optimization H Namkoong, A Sinha, S Yadlowsky, JC Duchi International Conference on Machine Learning, 2574-2583, 2017	47	2017
Assessing External Validity Over Worst-case Subpopulations S Jeong, H Namkoong Conference on Learning Theory, 2079-2084, 2020	35*	2020
Diagnosing model performance under distribution shift TT Cai, H Namkoong, S Yadlowsky arXiv preprint arXiv:2303.02011, 2023	32	2023
On the need for a language describing distribution shifts: Illustrations on tabular datasets J Liu, T Wang, P Cui, H Namkoong Advances in Neural Information Processing Systems 36, 2024	27	2024

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