Hongseok Namkoong

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 38

Top Topics

Robust Optimization

Causal Inference

Covariate Shift

Top Venues

Publications

Tiffany Tianhui Cai, Yuri Fonseca, Kaiwen Hou, Hongseok Namkoong
C-Learner: Constrained Learning for Causal Inference and Semiparametric Statistics. CoRR (2024)
Mike Li, Hongseok Namkoong, Shangzhou Xia
Evaluating Model Performance Under Worst-case Subpopulations. CoRR (2024)
Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo
Posterior Sampling via Autoregressive Generation. CoRR (2024)
Jiung Lee, Hongseok Namkoong, Yibo Zeng
Design and Scheduling of an AI-based Queueing System. CoRR (2024)
Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. CoRR (2023)
Tiffany Cai, Hongseok Namkoong, Steve Yadlowsky
Diagnosing Model Performance Under Distribution Shift. CoRR (2023)
Ethan Che, Hongseok Namkoong
Adaptive Experimentation at Scale: Bayesian Algorithms for Flexible Batches. CoRR (2023)
Ariel Boyarsky, Hongseok Namkoong, Jean Pouget-Abadie
Modeling Interference Using Experiment Roll-out. EC (2023)
John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
Distributionally Robust Losses for Latent Covariate Mixtures. Oper. Res. 71 (2) (2023)
Brian Hsu, Xiaotong Chen, Ying Han, Hongseok Namkoong, Kinjal Basu
An Operational Perspective to Fairness Interventions: Where and How to Intervene. CoRR (2023)
Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. NeurIPS (2023)
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)
Mitchell Wortsman, Gabriel Ilharco, Samir Ya 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)
Hongseok Namkoong, Yuanzhe Ma, Peter W. Glynn
Minimax Optimal Estimation of Stability Under Distribution Shift. CoRR (2022)
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 (2022)
John C. Duchi, Peter W. Glynn, Hongseok Namkoong
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach. Math. Oper. Res. 46 (3) (2021)
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 (2021)
Mike Li, Hongseok Namkoong, Shangzhou Xia
Evaluating model performance under worst-case subpopulations. NeurIPS (2021)
Sookyo Jeong, Hongseok Namkoong
Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects. CoRR (2020)
Hongseok Namkoong, Samuel Daulton, Eytan Bakshy
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. CoRR (2020)
John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
Distributionally Robust Losses for Latent Covariate Mixtures. CoRR (2020)
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. NeurIPS (2020)
Sookyo Jeong, Hongseok Namkoong
Robust causal inference under covariate shift via worst-case subpopulation treatment effects. COLT (2020)
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. CoRR (2020)
John C. Duchi, Hongseok Namkoong
Variance-based Regularization with Convex Objectives. J. Mach. Learn. Res. 20 (2019)
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
Fairness Without Demographics in Repeated Loss Minimization. ICML (2018)
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)
Matthew O'Kelly, Aman Sinha, Justin Norden, Hongseok Namkoong
In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation. CoRR (2018)
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
Generalizing to Unseen Domains via Adversarial Data Augmentation. NeurIPS (2018)
Aman Sinha, Hongseok Namkoong, John C. Duchi
Certifying Some Distributional Robustness with Principled Adversarial Training. ICLR (2018)
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
Fairness Without Demographics in Repeated Loss Minimization. CoRR (2018)
Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, John C. Duchi, Russ Tedrake
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. CoRR (2018)
John C. Duchi, Hongseok Namkoong
Learning Models with Uniform Performance via Distributionally Robust Optimization. CoRR (2018)
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
Generalizing to Unseen Domains via Adversarial Data Augmentation. CoRR (2018)
Aman Sinha, Hongseok Namkoong, John C. Duchi
Certifiable Distributional Robustness with Principled Adversarial Training. CoRR (2017)
Hongseok Namkoong, John C. Duchi
Variance-based Regularization with Convex Objectives. NIPS (2017)
Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi
Adaptive Sampling Probabilities for Non-Smooth Optimization. ICML (2017)
Hongseok Namkoong, John C. Duchi
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences. NIPS (2016)