Hongseok Namkoong

According to our database, Hongseok Namkoong authored at least 36 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems.

Brian Hsu, Cyrus DiCiccio, Natesh Sivasubramoniapillai, 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

QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers.

Haozhe Chen, Ang Li, Ethan Che, Tianyi Peng, Jing Dong, Hongseok Namkoong

CoRR, 2024

PersonalLLM: Tailoring LLMs to Individual Preferences.

Thomas P. Zollo, Andrew Wei Tung Siah, Naimeng Ye, Ang Li, Hongseok Namkoong

CoRR, 2024

Differentiable Discrete Event Simulation for Queuing Network Control.

Ethan Che, Jing Dong, Hongseok Namkoong

CoRR, 2024

Mathematical Programming For Adaptive Experiments.

Ethan Che, Daniel R. Jiang, Hongseok Namkoong, Jimmy Wang

CoRR, 2024

AExGym: Benchmarks and Environments for Adaptive Experimentation.

Jimmy Wang, Ethan Che, Daniel R. Jiang, Hongseok Namkoong

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

Design and Scheduling of an AI-based Queueing System.

Jiung Lee, Hongseok Namkoong, Yibo Zeng

CoRR, 2024

Posterior Sampling via Autoregressive Generation.

Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo

CoRR, 2024

C-Learner: Constrained Learning for Causal Inference and Semiparametric Statistics.

Tiffany Tianhui Cai, Yuri Fonseca, Kaiwen Hou, Hongseok Namkoong

CoRR, 2024

2023

Distributionally Robust Losses for Latent Covariate Mixtures.

John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong

Oper. Res., March, 2023

Adaptive Experimentation at Scale: Bayesian Algorithms for Flexible Batches.

Ethan Che, Hongseok Namkoong

CoRR, 2023

Diagnosing Model Performance Under Distribution Shift.

Tiffany Tianhui Cai, Hongseok Namkoong, Steve Yadlowsky

CoRR, 2023

An Operational Perspective to Fairness Interventions: Where and How to Intervene.

Brian Hsu, Xiaotong Chen, Ying Han, Hongseok Namkoong, Kinjal Basu

CoRR, 2023

Modeling Interference Using Experiment Roll-out.

Ariel Boyarsky, Hongseok Namkoong, Jean Pouget-Abadie

Proceedings of the 24th ACM Conference on Economics and Computation, 2023

On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets.

Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Minimax Optimal Estimation of Stability Under Distribution Shift.

Hongseok Namkoong, Yuanzhe Ma, Peter W. Glynn

CoRR, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

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, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

CoRR, 2021

Evaluating model performance under worst-case subpopulations.

Mike Li, Hongseok Namkoong, Shangzhou Xia

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning.

Hongseok Namkoong, Samuel Daulton, Eytan Bakshy

CoRR, 2020

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding.

Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust causal inference under covariate shift via worst-case subpopulation treatment effects.

Sookyo Jeong, Hongseok Namkoong

Proceedings of the Conference on Learning Theory, 2020

2019

Variance-based Regularization with Convex Objectives.

John C. Duchi, Hongseok Namkoong

J. Mach. Learn. Res., 2019

2018

In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation.

Matthew O'Kelly, Aman Sinha, Justin Norden, Hongseok Namkoong

CoRR, 2018

Learning Models with Uniform Performance via Distributionally Robust Optimization.

John C. Duchi, Hongseok Namkoong

CoRR, 2018

Generalizing to Unseen Domains via Adversarial Data Augmentation.

Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation.

Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Fairness Without Demographics in Repeated Loss Minimization.

Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang

Proceedings of the 35th International Conference on Machine Learning, 2018

Certifying Some Distributional Robustness with Principled Adversarial Training.

Aman Sinha, Hongseok Namkoong, John C. Duchi

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Certifiable Distributional Robustness with Principled Adversarial Training.

Aman Sinha, Hongseok Namkoong, John C. Duchi

CoRR, 2017

Adaptive Sampling Probabilities for Non-Smooth Optimization.

Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences.

Hongseok Namkoong, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016