Namkoong, Hongseok

32 publications

TMLR 2026 Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning Hongseok Namkoong, Samuel Daulton, Eytan Bakshy

ICLR 2026 FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning Liang Hu, Jianpeng Jiao, Jiashuo Liu, Dongyuan Mutu, Yanle Ren, Zhoufutu Wen, Kaiyuan Zhang, Xuanliang Zhang, Xiang Gao, Tianci He, Fei Hu, Yali Liao, Zaiyuan Wang, Jingkai Liu, Sun Daibin, Ziqing Zeng, Zhiyuan Zeng, Chenghao Yang, Qianyu Yang, Mingren Yin, Ge Zhang, Xinyi Zhang, Xiying Zhao, Zhu Zhenwei, Hongseok Namkoong, Wenhao Huang

ICLRW 2025 ADSO: Adaptive Data Mixture & Scale Optimization. a Multi-Scale Multi-Fidelity Bayesian Optimization Approach. Andrew Wei Tung Siah, Haozhe Chen, C. Daniel Guetta, Tianyi Peng, Hongseok Namkoong, Tzu-Ching Yen

ICML 2025 Adaptive Elicitation of Latent Information Using Natural Language Jimmy Wang, Thomas P Zollo, Richard Zemel, Hongseok Namkoong

ICLRW 2025 Adaptive Elicitation of Latent Information Using Natural Language Jimmy Wang, Thomas P Zollo, Richard Zemel, Hongseok Namkoong

NeurIPS 2025 Architectural and Inferential Inductive Biases for Exchangeable Sequence Modeling Daksh Mittal, Ang Li, Thomson Yen, C. Daniel Guetta, Hongseok Namkoong

NeurIPS 2025 Contextual Thompson Sampling via Generation of Missing Data Kelly W. Zhang, Tiffany Cai, Hongseok Namkoong, Daniel Russo

NeurIPS 2025 Data Mixture Optimization: A Multi-Fidelity Multi-Scale Bayesian Framework Thomson Yen, Andrew Wei Tung Siah, Haozhe Chen, C. Daniel Guetta, Tianyi Peng, Hongseok Namkoong

NeurIPS 2025 LLM Generated Persona Is a Promise with a Catch Ang Li, Haozhe Chen, Hongseok Namkoong, Tianyi Peng

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

NeurIPS 2024 Adaptive Labeling for Efficient Out-of-Distribution Model Evaluation Daksh Mittal, Yuanzhe Ma, Shalmali Joshi, Hongseok Namkoong

NeurIPSW 2024 LLM Embeddings Improve Test-Time Adaptation to Tabular $Y|X$-Shifts Yibo Zeng, Jiashuo Liu, Henry Lam, Hongseok Namkoong

NeurIPSW 2024 PersonalLLM: Tailoring LLMs to Individual Preferences Thomas P Zollo, Andrew Wei Tung Siah, Naimeng Ye, Ang Li, Hongseok Namkoong

NeurIPSW 2024 Posterior Sampling via Autoregressive Generation Kelly W. Zhang, Tiffany Cai, Hongseok Namkoong, Daniel Russo

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

ICMLW 2023 Dynamic Control of Queuing Networks via Differentiable Discrete-Event Simulation Ethan Che, Hongseok Namkoong, Jing Dong

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

NeurIPSW 2023 Planning Contextual Adaptive Experiments with Model Predictive Control Ethan Che, Jimmy Wang, Hongseok Namkoong

ICML 2022 Model Soups: Averaging Weights of Multiple Fine-Tuned Models Improves Accuracy Without Increasing Inference Time 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

CVPR 2022 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

NeurIPS 2021 Evaluating Model Performance Under Worst-Case Subpopulations Mike Li, Hongseok Namkoong, Shangzhou Xia

NeurIPSW 2021 Robust Fine-Tuning of Zero-Shot Models Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hanna Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

NeurIPS 2020 Off-Policy Policy Evaluation for Sequential Decisions Under Unobserved Confounding Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill

COLT 2020 Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects Sookyo Jeong, Hongseok Namkoong

JMLR 2019 Variance-Based Regularization with Convex Objectives John Duchi, Hongseok Namkoong

ICLR 2018 Certifying Some Distributional Robustness with Principled Adversarial Training Aman Sinha, Hongseok Namkoong, John Duchi

ICML 2018 Fairness Without Demographics in Repeated Loss Minimization Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang

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 Scalable End-to-End Autonomous Vehicle Testing via Rare-Event Simulation Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi

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

NeurIPS 2017 Variance-Based Regularization with Convex Objectives Hongseok Namkoong, John C. Duchi

NeurIPS 2016 Stochastic Gradient Methods for Distributionally Robust Optimization with F-Divergences Hongseok Namkoong, John C. Duchi