Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences. - Hongseok Namkoong; John C. Duchi (NIPS, 2016)

Adaptive Sampling Probabilities for Non-Smooth Optimization. - Hongseok Namkoong; Aman Sinha 001; Steve Yadlowsky; John C. Duchi (ICML, 2017)

Certifiable Distributional Robustness with Principled Adversarial Training. - Aman Sinha 001; Hongseok Namkoong; John C. Duchi (CoRR, 2017)

Certifying Some Distributional Robustness with Principled Adversarial Training. - Aman Sinha 001; Hongseok Namkoong; John C. Duchi (ICLR, 2018)

Fairness Without Demographics in Repeated Loss Minimization. - Tatsunori B. Hashimoto; Megha Srivastava; Hongseok Namkoong; Percy Liang (ICML, 2018)

Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. - Matthew O'Kelly; Aman Sinha 001; Hongseok Namkoong; Russ Tedrake; John C. Duchi (NeurIPS, 2018)

Generalizing to Unseen Domains via Adversarial Data Augmentation. - Riccardo Volpi; Hongseok Namkoong; Ozan Sener; John C. Duchi; Vittorio Murino; Silvio Savarese (NeurIPS, 2018)

Learning Models with Uniform Performance via Distributionally Robust Optimization. - John C. Duchi; Hongseok Namkoong (CoRR, 2018)

In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation. - Matthew O'Kelly; Aman Sinha 001; Justin Norden; Hongseok Namkoong (CoRR, 2018)

Variance-based Regularization with Convex Objectives. - John C. Duchi; Hongseok Namkoong (J. Mach. Learn. Res., 2019)

Robust causal inference under covariate shift via worst-case subpopulation treatment effects. - Sookyo Jeong; Hongseok Namkoong (COLT, 2020)

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. - Hongseok Namkoong; Ramtin Keramati; Steve Yadlowsky; Emma Brunskill (NeurIPS, 2020)

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. - Hongseok Namkoong; Samuel Daulton; Eytan Bakshy (CoRR, 2020)

Evaluating model performance under worst-case subpopulations. - Mike Li; Hongseok Namkoong; Shangzhou Xia (NeurIPS, 2021)

Robust fine-tuning of zero-shot models. - Mitchell Wortsman; Gabriel Ilharco; Mike Li; Jong Wook Kim; Hannaneh Hajishirzi; Ali Farhadi; Hongseok Namkoong; Ludwig Schmidt (CoRR, 2021)

Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach. - John C. Duchi; Peter W. Glynn; Hongseok Namkoong (Math. Oper. Res., 2021)

Robust fine-tuning of zero-shot models. - Mitchell Wortsman; Gabriel Ilharco; Jong Wook Kim; Mike Li; Simon Kornblith; Rebecca Roelofs; Raphael Gontijo Lopes; Hannaneh Hajishirzi; Ali Farhadi; Hongseok Namkoong; Ludwig Schmidt (CVPR, 2022)

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. - 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 (ICML, 2022)

Minimax Optimal Estimation of Stability Under Distribution Shift. - Hongseok Namkoong; Yuanzhe Ma; Peter W. Glynn (CoRR, 2022)

On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. - Jiashuo Liu; Tianyu Wang; Peng Cui 001; Hongseok Namkoong (NeurIPS, 2023)

Modeling Interference Using Experiment Roll-out. - Ariel Boyarsky; Hongseok Namkoong; Jean Pouget-Abadie (EC, 2023)

An Operational Perspective to Fairness Interventions: Where and How to Intervene. - Brian Hsu; Xiaotong Chen; Ying Han; Hongseok Namkoong; Kinjal Basu 001 (CoRR, 2023)

Diagnosing Model Performance Under Distribution Shift. - Tiffany Tianhui Cai; Hongseok Namkoong; Steve Yadlowsky (CoRR, 2023)

Adaptive Experimentation at Scale: Bayesian Algorithms for Flexible Batches. - Ethan Che; Hongseok Namkoong (CoRR, 2023)

Distributionally Robust Losses for Latent Covariate Mixtures. - John C. Duchi; Tatsunori Hashimoto; Hongseok Namkoong (Oper. Res., 2023)

QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers. - Haozhe Chen; Ang Li; Ethan Che; Jing Dong; Tianyi Peng; Hongseok Namkoong (NeurIPS, 2024)

Adaptive Labeling for Efficient Out-of-distribution Model Evaluation. - Daksh Mittal; Yuanzhe Ma; Shalmali Joshi; Hongseok Namkoong (NeurIPS, 2024)

C-Learner: Constrained Learning for Causal Inference and Semiparametric Statistics. - Tiffany Tianhui Cai; Yuri Fonseca; Kaiwen Hou; Hongseok Namkoong (CoRR, 2024)

Posterior Sampling via Autoregressive Generation. - Kelly W. Zhang; Tiffany Tianhui Cai; Hongseok Namkoong; Daniel Russo 001 (CoRR, 2024)

Design and Scheduling of an AI-based Queueing System. - Jiung Lee; Hongseok Namkoong; Yibo Zeng (CoRR, 2024)

Pre-training and in-context learning IS Bayesian inference a la De Finetti. - Naimeng Ye; Hanming Yang; Andrew Siah; Hongseok Namkoong (CoRR, 2024)

AExGym: Benchmarks and Environments for Adaptive Experimentation. - Jimmy Wang; Ethan Che; Daniel R. Jiang; Hongseok Namkoong (CoRR, 2024)

Mathematical Programming For Adaptive Experiments. - Ethan Che; Daniel R. Jiang; Hongseok Namkoong; Jimmy Wang (CoRR, 2024)

Differentiable Discrete Event Simulation for Queuing Network Control. - Ethan Che; Jing Dong; Hongseok Namkoong (CoRR, 2024)

LLM Embeddings Improve Test-time Adaptation to Tabular Y|X-Shifts. - Yibo Zeng; Jiashuo Liu; Henry Lam; Hongseok Namkoong (CoRR, 2024)

From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems. - Brian Hsu; Cyrus DiCiccio; Natesh Sivasubramoniapillai; Hongseok Namkoong (CoRR, 2024)

PersonalLLM: Tailoring LLMs to Individual Preferences. - Thomas P. Zollo; Andrew Wei Tung Siah; Naimeng Ye; Ang Li; Hongseok Namkoong (ICLR, 2025)

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. - Shalmali Joshi; Iñigo Urteaga; Wouter A. C. van Amsterdam; George Hripcsak; Pierre A. Elias; Benjamin R. C. Amor; Noémie Elhadad; James C. Fackler; Mark P. Sendak; Jenna Wiens; Kaivalya Deshpande; Yoav Wald; et al. (J. Am. Medical Informatics Assoc., 2025)

A Planning Framework for Adaptive Labeling. - Daksh Mittal; Yuanzhe Ma; Shalmali Joshi; Hongseok Namkoong (CoRR, 2025)

Contextual Thompson Sampling via Generation of Missing Data. - Kelly W. Zhang; Tiffany Tianhui Cai; Hongseok Namkoong; Daniel Russo 001 (CoRR, 2025)

Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling. - Daksh Mittal; Ang Li; Tzu-Ching Yen; Daniel Guetta; Hongseok Namkoong (CoRR, 2025)

LLM Generated Persona is a Promise with a Catch. - Ang Li; Haozhe Chen; Hongseok Namkoong; Tianyi Peng (CoRR, 2025)

Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework. - Thomson Yen; Andrew Wei Tung Siah; Haozhe Chen; Tianyi Peng; Daniel Guetta; Hongseok Namkoong (CoRR, 2025)

Adaptive Elicitation of Latent Information Using Natural Language. - Jimmy Wang; Thomas P. Zollo; Richard S. Zemel; Hongseok Namkoong (CoRR, 2025)

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning. - Jiashuo Liu; Tianyu Wang; Henry Lam; Hongseok Namkoong; Jose H. Blanchet (CoRR, 2025)