Hongseok Namkoong

71 papers A* 15Journal 52Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Diagnosing Model Performance Under Distribution Shift. Oper. Res. Tiffany (tianhui) Cai, Hongseok Namkoong, Steve Yadlowsky
2026	J	`jnl`	Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. Trans. Mach. Learn. Res. Hongseok Namkoong, Samuel Daulton, Eytan Bakshy
2026	J	`jnl`	Empirical Likelihood for Nonsmooth Functionals. CoRR Hongseok Namkoong
2026	J	`jnl`	Minimax Optimal Estimation of Stability Under Distribution Shift. Oper. Res. Hongseok Namkoong, Yuanzhe Ma, Peter W. Glynn
2025	J	`jnl`	A Broader View of Thompson Sampling. CoRR Yanlin Qu, Hongseok Namkoong, Assaf Zeevi
2025	J	`jnl`	A Planning Framework for Adaptive Labeling. CoRR Daksh Mittal, Yuanzhe Ma, Shalmali Joshi, Hongseok Namkoong
2025	J	`jnl`	A Sensitivity Approach to Causal Inference Under Limited Overlap. CoRR Yuanzhe Ma, Hongseok Namkoong
2025	J	`jnl`	AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. J. Am. Medical Informatics Assoc. 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, Madalina Fiterau, Zachary C. Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E. Vogt, Rajesh Ranganath
2025	A*	`conf`	Adaptive Elicitation of Latent Information Using Natural Language. ICML Jimmy Wang, Thomas P. Zollo, Richard S. Zemel, Hongseok Namkoong
2025	J	`jnl`	Adaptive Elicitation of Latent Information Using Natural Language. CoRR Jimmy Wang, Thomas P. Zollo, Richard S. Zemel, Hongseok Namkoong
2025	J	`jnl`	Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling. CoRR Daksh Mittal, Ang Li, Tzu-Ching Yen, Daniel Guetta, Hongseok Namkoong
2025	J	`jnl`	Benchmarking In-context Experiential Learning Through Repeated Product Recommendations. CoRR Gilbert Yang, Yaqin Chen, Thomson Yen, Hongseok Namkoong
2025	J	`jnl`	Contextual Thompson Sampling via Generation of Missing Data. CoRR Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo
2025	J	`jnl`	DRO: A Python Library for Distributionally Robust Optimization in Machine Learning. CoRR Jiashuo Liu, Tianyu Wang, Henry Lam, Hongseok Namkoong, Jose H. Blanchet
2025	J	`jnl`	Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework. CoRR Thomson Yen, Andrew Wei Tung Siah, Haozhe Chen, Tianyi Peng, Daniel Guetta, Hongseok Namkoong
2025	J	`jnl`	Data-Driven Stochastic Modeling Using Autoregressive Sequence Models: Translating Event Tables to Queueing Dynamics. CoRR Daksh Mittal, Shunri Zheng, Jing Dong, Hongseok Namkoong
2025	J	`jnl`	FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning. CoRR Liang Hu, Jianpeng Jiao, Jiashuo Liu, Yanle Ren, Zhoufutu Wen, Kaiyuan Zhang, Xuanliang Zhang, Xiang Gao, Tianci He, Fei Hu, Yali Liao, Zaiyuan Wang, Chenghao Yang, Qianyu Yang, Mingren Yin, Zhiyuan Zeng, Ge Zhang, Xinyi Zhang, Xiying Zhao, Zhenwei Zhu, Hongseok Namkoong, Wenhao Huang, Yuwen Tang
2025	J	`jnl`	LLM Generated Persona is a Promise with a Catch. CoRR Ang Li, Haozhe Chen, Hongseok Namkoong, Tianyi Peng
2025	J	`jnl`	LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics. CoRR Jiashuo Liu, Jiayun Wu, Chunjie Wu, Jingkai Liu, Zaiyuan Wang, Zhou Huan, Wenhao Huang, Hongseok Namkoong
2025	J	`jnl`	Learning-To-Measure: In-context Active Feature Acquisition. CoRR Yuta Kobayashi, Zilin Jing, Jiayu Yao, Hongseok Namkoong, Shalmali Joshi
2025	A*	`conf`	PersonalLLM: Tailoring LLMs to Individual Preferences. ICLR Thomas P. Zollo, Andrew Wei Tung Siah, Naimeng Ye, Ang Li, Hongseok Namkoong
2025	J	`jnl`	SynthTools: A Framework for Scaling Synthetic Tools for Agent Development. CoRR Tommaso Castellani, Naimeng Ye, Daksh Mittal, Thomson Yen, Hongseok Namkoong
2024	J	`jnl`	AExGym: Benchmarks and Environments for Adaptive Experimentation. CoRR Jimmy Wang, Ethan Che, Daniel R. Jiang, Hongseok Namkoong
2024	A*	`conf`	Adaptive Labeling for Efficient Out-of-distribution Model Evaluation. NeurIPS Daksh Mittal, Yuanzhe Ma, Shalmali Joshi, Hongseok Namkoong
2024	J	`jnl`	C-Learner: Constrained Learning for Causal Inference and Semiparametric Statistics. CoRR Tiffany Tianhui Cai, Yuri Fonseca, Kaiwen Hou, Hongseok Namkoong
2024	J	`jnl`	Design and Scheduling of an AI-based Queueing System. CoRR Jiung Lee, Hongseok Namkoong, Yibo Zeng
2024	J	`jnl`	Differentiable Discrete Event Simulation for Queuing Network Control. CoRR Ethan Che, Jing Dong, Hongseok Namkoong
2024	J	`jnl`	Evaluating Model Performance Under Worst-case Subpopulations. CoRR Mike Li, Hongseok Namkoong, Shangzhou Xia
2024	J	`jnl`	From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems. CoRR Brian Hsu, Cyrus DiCiccio, Natesh Sivasubramoniapillai, Hongseok Namkoong
2024	—	`conf`	From Models to Systems: A Comprehensive Framework for AI System Fairness in Compositional Recommender Systems. AFME Brian Hsu, Cyrus DiCiccio, Natesh S. Pillai, Hongseok Namkoong
2024	J	`jnl`	LLM Embeddings Improve Test-time Adaptation to Tabular Y\|X-Shifts. CoRR Yibo Zeng, Jiashuo Liu, Henry Lam, Hongseok Namkoong
2024	J	`jnl`	Mathematical Programming For Adaptive Experiments. CoRR Ethan Che, Daniel R. Jiang, Hongseok Namkoong, Jimmy Wang
2024	J	`jnl`	PersonalLLM: Tailoring LLMs to Individual Preferences. CoRR Thomas P. Zollo, Andrew Wei Tung Siah, Naimeng Ye, Ang Li, Hongseok Namkoong
2024	J	`jnl`	Posterior Sampling via Autoregressive Generation. CoRR Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo
2024	J	`jnl`	Pre-training and in-context learning IS Bayesian inference a la De Finetti. CoRR Naimeng Ye, Hanming Yang, Andrew Siah, Hongseok Namkoong
2024	A*	`conf`	QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers. NeurIPS Haozhe Chen, Ang Li, Ethan Che, Jing Dong, Tianyi Peng, Hongseok Namkoong
2024	J	`jnl`	QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers. CoRR Haozhe Chen, Ang Li, Ethan Che, Tianyi Peng, Jing Dong, Hongseok Namkoong
2023	J	`jnl`	Adaptive Experimentation at Scale: Bayesian Algorithms for Flexible Batches. CoRR Ethan Che, Hongseok Namkoong
2023	J	`jnl`	An Operational Perspective to Fairness Interventions: Where and How to Intervene. CoRR Brian Hsu, Xiaotong Chen, Ying Han, Hongseok Namkoong, Kinjal Basu
2023	J	`jnl`	Diagnosing Model Performance Under Distribution Shift. CoRR Tiffany Tianhui Cai, Hongseok Namkoong, Steve Yadlowsky
2023	J	`jnl`	Distributionally Robust Losses for Latent Covariate Mixtures. Oper. Res. John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
2023	A*	`conf`	Modeling Interference Using Experiment Roll-out. EC Ariel Boyarsky, Hongseok Namkoong, Jean Pouget-Abadie
2023	A*	`conf`	On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. NeurIPS Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong
2023	J	`jnl`	On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. CoRR Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong
2022	J	`jnl`	Minimax Optimal Estimation of Stability Under Distribution Shift. CoRR Hongseok Namkoong, Yuanzhe Ma, Peter W. Glynn
2022	A*	`conf`	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. ICML 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
2022	J	`jnl`	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. CoRR 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
2022	A*	`conf`	Robust fine-tuning of zero-shot models. CVPR Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt
2021	A*	`conf`	Evaluating model performance under worst-case subpopulations. NeurIPS Mike Li, Hongseok Namkoong, Shangzhou Xia
2021	J	`jnl`	Robust fine-tuning of zero-shot models. CoRR Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt
2021	J	`jnl`	Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach. Math. Oper. Res. John C. Duchi, Peter W. Glynn, Hongseok Namkoong
2020	J	`jnl`	Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. CoRR Hongseok Namkoong, Samuel Daulton, Eytan Bakshy
2020	J	`jnl`	Distributionally Robust Losses for Latent Covariate Mixtures. CoRR John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
2020	A*	`conf`	Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. NeurIPS Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill
2020	J	`jnl`	Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. CoRR Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill
2020	J	`jnl`	Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects. CoRR Sookyo Jeong, Hongseok Namkoong
2020	A*	`conf`	Robust causal inference under covariate shift via worst-case subpopulation treatment effects. COLT Sookyo Jeong, Hongseok Namkoong
2019	J	`jnl`	Variance-based Regularization with Convex Objectives. J. Mach. Learn. Res. John C. Duchi, Hongseok Namkoong
2018	—	`conf`	Certifying Some Distributional Robustness with Principled Adversarial Training. ICLR Aman Sinha, Hongseok Namkoong, John C. Duchi
2018	A*	`conf`	Fairness Without Demographics in Repeated Loss Minimization. ICML Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
2018	J	`jnl`	Fairness Without Demographics in Repeated Loss Minimization. CoRR Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
2018	A*	`conf`	Generalizing to Unseen Domains via Adversarial Data Augmentation. NeurIPS Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
2018	J	`jnl`	Generalizing to Unseen Domains via Adversarial Data Augmentation. CoRR Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
2018	J	`jnl`	In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation. CoRR Matthew O'Kelly, Aman Sinha, Justin Norden, Hongseok Namkoong
2018	J	`jnl`	Learning Models with Uniform Performance via Distributionally Robust Optimization. CoRR John C. Duchi, Hongseok Namkoong
2018	A*	`conf`	Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. NeurIPS Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi
2018	J	`jnl`	Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. CoRR Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, John C. Duchi, Russ Tedrake
2017	A*	`conf`	Adaptive Sampling Probabilities for Non-Smooth Optimization. ICML Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi
2017	J	`jnl`	Certifiable Distributional Robustness with Principled Adversarial Training. CoRR Aman Sinha, Hongseok Namkoong, John C. Duchi
2017	—	`conf`	Variance-based Regularization with Convex Objectives. NIPS Hongseok Namkoong, John C. Duchi
2016	—	`conf`	Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences. NIPS Hongseok Namkoong, John C. Duchi