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Hongseok Namkoong
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2020 – today
- 2024
- [i28]Tiffany Tianhui Cai, Yuri Fonseca, Kaiwen Hou, Hongseok Namkoong:
C-Learner: Constrained Learning for Causal Inference and Semiparametric Statistics. CoRR abs/2405.09493 (2024) - [i27]Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo:
Posterior Sampling via Autoregressive Generation. CoRR abs/2405.19466 (2024) - [i26]Jiung Lee, Hongseok Namkoong, Yibo Zeng:
Design and Scheduling of an AI-based Queueing System. CoRR abs/2406.06855 (2024) - [i25]Mike Li, Hongseok Namkoong, Shangzhou Xia:
Evaluating Model Performance Under Worst-case Subpopulations. CoRR abs/2407.01316 (2024) - [i24]Naimeng Ye, Hanming Yang, Andrew Siah, Hongseok Namkoong:
Pre-training and in-context learning IS Bayesian inference a la De Finetti. CoRR abs/2408.03307 (2024) - [i23]Jimmy Wang, Ethan Che, Daniel R. Jiang, Hongseok Namkoong:
AExGym: Benchmarks and Environments for Adaptive Experimentation. CoRR abs/2408.04531 (2024) - [i22]Ethan Che, Daniel R. Jiang, Hongseok Namkoong, Jimmy Wang:
Mathematical Programming For Adaptive Experiments. CoRR abs/2408.04570 (2024) - [i21]Ethan Che, Jing Dong, Hongseok Namkoong:
Differentiable Discrete Event Simulation for Queuing Network Control. CoRR abs/2409.03740 (2024) - [i20]Thomas P. Zollo, Andrew Wei Tung Siah, Naimeng Ye, Ang Li, Hongseok Namkoong:
PersonalLLM: Tailoring LLMs to Individual Preferences. CoRR abs/2409.20296 (2024) - [i19]Haozhe Chen, Ang Li, Ethan Che, Tianyi Peng, Jing Dong, Hongseok Namkoong:
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers. CoRR abs/2410.06170 (2024) - [i18]Yibo Zeng, Jiashuo Liu, Henry Lam, Hongseok Namkoong:
LLM Embeddings Improve Test-time Adaptation to Tabular Y|X-Shifts. CoRR abs/2410.07395 (2024) - 2023
- [j3]John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
:
Distributionally Robust Losses for Latent Covariate Mixtures. Oper. Res. 71(2): 649-664 (2023) - [c14]Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong:
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. NeurIPS 2023 - [c13]Ariel Boyarsky
Modeling Interference Using Experiment Roll-out. EC 2023: 298 - [i17]Brian Hsu, Xiaotong Chen, Ying Han, Hongseok Namkoong, Kinjal Basu:
An Operational Perspective to Fairness Interventions: Where and How to Intervene. CoRR abs/2302.01574 (2023) - [i16]Tiffany Tianhui Cai, Hongseok Namkoong, Steve Yadlowsky:
Diagnosing Model Performance Under Distribution Shift. CoRR abs/2303.02011 (2023) - [i15]Ethan Che, Hongseok Namkoong:
Adaptive Experimentation at Scale: Bayesian Algorithms for Flexible Batches. CoRR abs/2303.11582 (2023) - [i14]Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong:
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. CoRR abs/2307.05284 (2023) - 2022
- [c12]Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt:
Robust fine-tuning of zero-shot models. CVPR 2022: 7949-7961 - [c11]Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt:
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. ICML 2022: 23965-23998 - [i13]Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt:
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. CoRR abs/2203.05482 (2022) - [i12]Hongseok Namkoong, Yuanzhe Ma, Peter W. Glynn:
Minimax Optimal Estimation of Stability Under Distribution Shift. CoRR abs/2212.06338 (2022) - 2021
- [j2]John C. Duchi, Peter W. Glynn, Hongseok Namkoong
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach. Math. Oper. Res. 46(3): 946-969 (2021) - [c10]Mike Li, Hongseok Namkoong, Shangzhou Xia:
Evaluating model performance under worst-case subpopulations. NeurIPS 2021: 17325-17334 - [i11]Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt:
Robust fine-tuning of zero-shot models. CoRR abs/2109.01903 (2021) - 2020
- [c9]Sookyo Jeong, Hongseok Namkoong:
Robust causal inference under covariate shift via worst-case subpopulation treatment effects. COLT 2020: 2079-2084 - [c8]Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill:
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. NeurIPS 2020 - [i10]Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill:
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. CoRR abs/2003.05623 (2020) - [i9]Sookyo Jeong, Hongseok Namkoong:
Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects. CoRR abs/2007.02411 (2020) - [i8]John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong:
Distributionally Robust Losses for Latent Covariate Mixtures. CoRR abs/2007.13982 (2020) - [i7]Hongseok Namkoong, Samuel Daulton, Eytan Bakshy:
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. CoRR abs/2011.14266 (2020)
2010 – 2019
- 2019
- [j1]John C. Duchi, Hongseok Namkoong:
Variance-based Regularization with Convex Objectives. J. Mach. Learn. Res. 20: 68:1-68:55 (2019) - 2018
- [c7]Aman Sinha, Hongseok Namkoong, John C. Duchi:
Certifying Some Distributional Robustness with Principled Adversarial Training. ICLR 2018 - [c6]Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang:
Fairness Without Demographics in Repeated Loss Minimization. ICML 2018: 1934-1943 - [c5]Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese:
Generalizing to Unseen Domains via Adversarial Data Augmentation. NeurIPS 2018: 5339-5349 - [c4]Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi:
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. NeurIPS 2018: 9849-9860 - [i6]Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese:
Generalizing to Unseen Domains via Adversarial Data Augmentation. CoRR abs/1805.12018 (2018) - [i5]Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang:
Fairness Without Demographics in Repeated Loss Minimization. CoRR abs/1806.08010 (2018) - [i4]John C. Duchi, Hongseok Namkoong:
Learning Models with Uniform Performance via Distributionally Robust Optimization. CoRR abs/1810.08750 (2018) - [i3]Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, John C. Duchi, Russ Tedrake:
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. CoRR abs/1811.00145 (2018) - [i2]Matthew O'Kelly, Aman Sinha, Justin Norden, Hongseok Namkoong:
In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation. CoRR abs/1812.00293 (2018) - 2017
- [c3]Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi:
Adaptive Sampling Probabilities for Non-Smooth Optimization. ICML 2017: 2574-2583 - [c2]Hongseok Namkoong, John C. Duchi:
Variance-based Regularization with Convex Objectives. NIPS 2017: 2971-2980 - [i1]Aman Sinha, Hongseok Namkoong, John C. Duchi:
Certifiable Distributional Robustness with Principled Adversarial Training. CoRR abs/1710.10571 (2017) - 2016
- [c1]Hongseok Namkoong, John C. Duchi:
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences. NIPS 2016: 2208-2216
Coauthor Index
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