@article{ZengLiLaNa24,title={{LLM} Embeddings Improve Test-time Adaptation to Tabular $Y|X$-Shifts},author={Zeng$*$, Yibo and Liu$*$, Jiashuo and Lam, Henry and Namkoong, Hongseok},journal={arXiv:2410.07395 [cs.LG]},year={2024},url={https://arxiv.org/abs/2410.07395},}
@inproceedings{ChenLiChPeDoNa24,title={{QGym}: Scalable Simulation and Benchmarking of Queuing Network Controllers},author={Chen, Haozhe and Li, Ang and Che, Ethan and Peng, Tianyi and Dong, Jing and Namkoong, Hongseok},booktitle={Advances in Neural Information Processing Systems 37, Datasets and Benchmark Track},year={2024},url={https://arxiv.org/abs/2410.06170},}
@article{ZolloSiYeLiNa24,title={{PersonalLLM}: Tailoring {LLMs} to Individual Preferences},author={Zollo$*$, Thomas and Siah$*$, Andrew and Ye, Naimeng and Li, Ang and Namkoong, Hongseok},journal={arXiv:2409.20296 [cs.LG]},year={2024},url={https://www.arxiv.org/abs/2409.20296},}
Selected for oral presentations at the Econometric Society
Interdisciplinary Frontiers: Economics and AI+ML
conference and Conference on Digital Experimentation
@article{CheJiNaWa24,title={Optimization-Driven Adaptive Experimentation},author={Che, Ethan and Jiang, Daniel and Namkoong, Hongseok and Wang, Jimmy},journal={arXiv:2408.04570 [cs.LG]},year={2024},note={Selected for oral presentations at the Econometric Society
Interdisciplinary Frontiers: Economics and AI+ML
conference and Conference on Digital Experimentation},url={https://arxiv.org/abs/2408.04570},}
@article{WangChJiNa24,title={{AExGym}: Benchmarks and Environments for Adaptive Experimentation},author={Wang, Jimmy and Che, Ethan and Jiang, Daniel and Namkoong, Hongseok},journal={arXiv:2408.04531 [cs.LG]},year={2024},url={https://arxiv.org/abs/2408.04531},}
@article{YeYaSiNa24,title={Pre-training and in-context learning IS Bayesian inference a la De Finetti},author={Ye, Naimeng and Yang, Hanming and Siah, Andrew and Namkoong, Hongseok},journal={arXiv:2408.03307 [stat.ML]},year={2024},url={https://arxiv.org/abs/2408.03307}}
@article{CaiFoHoNa24,title={Constrained Learning for Causal Inference and Semiparametric Statistics},author={Cai$*$, Tiffany and Fonseca$*$, Yuri and Hou, Kaiwen and Namkoong, Hongseok},journal={arXiv:2405.09493 [stat.ML]},year={2024},url={https://arxiv.org/abs/2405.09493}}
@article{ZhangCaNaRu24,title={Posterior Sampling via Autoregressive Generation},author={Zhang$*$, Kelly and Cai$*$, Tiffany and Namkoong, Hongseok and Russo, Daniel},journal={arXiv:2405.19466 [cs.LG]},year={2024},note={Selected for presentation at the Econometric Society
Interdisciplinary Frontiers: Economics and AI+ML
conference},url={https://arxiv.org/abs/2405.19466},}
Recent advances in AI present significant opportunities to
rethink the design of service systems with AI at the
forefront. Even in the era of LLMs, managing a
workforce of human agents (“servers”) is a crit-
ical problem. Crowdsourcing workers are vital for
aligning LLMs with human values (e.g., ChatGPT) and
in many domains, the cost of human annotation is a
binding constraint (e.g., medical diagnosis from
radiologists). This work models and analyzes modern
service systems involving human reviewers and
state-of-the-art AI models. A key intellectual
challenge in managing con- gestion within such
service systems is endogeneity. Prediction is never
the goal, and the link between predictive
performance and downstream decision-making
performance is not straightforward due to
endogeneity. Our work crystallizes how classical
tools from queueing theory provide managerial
insights into the design of AI-based service
systems.
@article{LeeNaZe24,title={Design and Scheduling of an AI-based Queueing System},author={Lee, Jiung and Namkoong, Hongseok and Zeng, Yibo},year={2024},journal={arXiv:2406.06855 [math.OC]},url={https://arxiv.org/abs/2406.06855}}
Different distribution shifts require different interventions, and algorithms must be grounded in the specific shifts they address. Advocating for an inductive approach to research on distributional robustness, we build an empirical testbed, "WhyShift", comprising of natural shifts across 5 tabular datasets and 60,000 model configurations encompassing imbalanced learning algorithms and distributionally robust optimization (DRO) methods. We find Y|X-shifts are most prevalent on our testbed, in stark contrast to the heavy focus on X (covariate)-shifts in the ML literature. We conduct
an in-depth empirical analysis of DRO methods and find that the underlying model class (e.g.,
neural networks, XGBoost) and hyperparameter selection have a first-order impact in practice
despite being overlooked by DRO researchers. To further bridge that gap between methodological
research and practice, we design case studies that illustrate how such a refined understanding of
distribution shifts can enhance both data-centric and algorithmic interventions.
@article{LiuWaCuNa24,title={On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets},author={Liu$*$, Jiashuo and Wang$*$, Tianyu and Cui, Peng and Namkoong, Hongseok},year={2024},journal={arXiv:2307.05284 [cs.LG]},url={https://arxiv.org/abs/2307.05284},note={Conference version appeared in NeurIPS 2023.},}
Starting with my one-year stint at Meta’s adaptive
experimentation team, I’ve been pondering on how
bandit algorithms are largely designed by
theoreticians to achieve good regret bounds and are
rarely used in practice due to the difficulty of
implementation and poor empirical performance. In
this work, we focus on underpowered, short-horizon,
and large-batch problems that typically arise in
practice. We use large batch normal approximations
to derive an MDP formulation for deriving the
optimal adaptive design. Our formulation allows the
use of computational tools for designing adaptive
algorithms, a break from the existing theory-driven
paradigm.
Our approach significantly improves statistical power over standard
methods, even when compared to Bayesian bandit algorithms
(e.g., Thompson sampling) that require full distributional knowledge
of individual rewards. Overall, we expand the scope of
adaptive experimentation to settings that are difficult
for standard methods, involving limited adaptivity,
low signal-to-noise ratio, and unknown reward distributions.
@article{CheNa23,title={Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches},author={Che, Ethan and Namkoong, Hongseok},year={2023},journal={arXiv:2303.11582 [cs.LG]},note={Major revision in Operations Research},url={https://arxiv.org/abs/2303.11582},}
Recent advances in AI present significant opportunities to
rethink the design of service systems with AI at the
forefront. Even in the era of LLMs, managing a
workforce of human agents (“servers”) is a crit-
ical problem. Crowdsourcing workers are vital for
aligning LLMs with human values (e.g., ChatGPT) and
in many domains, the cost of human annotation is a
binding constraint (e.g., medical diagnosis from
radiologists). This work models and analyzes modern
service systems involving human reviewers and
state-of-the-art AI models. A key intellectual
challenge in managing con- gestion within such
service systems is endogeneity. Prediction is never
the goal, and the link between predictive
performance and downstream decision-making
performance is not straightforward due to
endogeneity. Our work crystallizes how classical
tools from queueing theory provide managerial
insights into the design of AI-based service
systems.
@article{CaiNaYa23,title={Diagnosing Model Performance Under Distribution Shift},author={Cai, Tiffany and Namkoong, Hongseok and Yadlowsky, Steve},year={2023},journal={arXiv:2303.02011 [stat.ML]},note={Major revision in Operations Research; Conference version appeared Symposium on Foundations of Responsible Computing 2023},url={https://arxiv.org/abs/2303.02011},}
@article{BoyarskyNaPo23,title={Modeling Interference via Experiment Rollout},author={Boyarsky, Ari and Namkoong, Hongseok and Pouget-Abadie, Jean},year={2023},journal={arXiv:2305.10728 [stat.ME]},note={Conference version appeared in ACM conference on Economics and Computation},url={https://arxiv.org/abs/2305.10728},}
@article{NamkoongDaBa24,title={Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning},author={Namkoong$*$, Hongseok and Daulton$*$, Samuel and Bakshy, Eytan},journal={Major revision in Manufacturing \& Service Operations Management},year={2024},note={Selected for an oral presentation at the Neurips 2020 OfflineRL Workshop},url={https://arxiv.org/abs/2011.14266},}
@article{JeongNa22,title={Assessing External Validity via Worst-case Subpopulation Treatment Effects},author={Jeong, Sookyo and Namkoong, Hongseok},journal={arXiv:2007.02411 [stat.ML]},year={2022},note={Short version appeared in Conference on Learning Theory 2020},url={https://arxiv.org/abs/2007.02411},}
@inproceedings{WortsmanIlGaRoGoMoNaFaCaKoSc22,title={Model Soups: Averaging Weights of Multiple Fine-tuned Models Improves Accuracy Without Increasing Inference Time},author={Wortsman, Mitchell and Ilharco, Gabriel and Gadre, Samir Yitzhak and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Morcos, Ari S and Namkoong, Hongseok and Farhadi, Ali and Carmon, Yair and Kornblith, Simon and Schmidt, Ludwig},booktitle={Proceedings of the 39th International Conference on Machine Learning},year={2022},url={https://proceedings.mlr.press/v162/wortsman22a/wortsman22a.pdf},}
@inproceedings{WortsmanIlLiKiHaFaNaSc22,title={Robust Fine-tuning of Zero-shot Models},author={Wortsman$*$, Mitchell and Ilharco$*$, Gabriel and Kim, Jong Wook and Li, Mike and Kornblith, Simon and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Hajishirzi, Hannaneh and Farhadi, Ali and Namkoong, Hongseok and Schmidt, Ludwig},booktitle={Proceedings of the 32nd IEEE Conference on Computer Vision and Pattern Recognition},note={CVPR Best Paper Finalist},year={2022},url={https://openaccess.thecvf.com/content/CVPR2022/papers/Wortsman_Robust_Fine-Tuning_of_Zero-Shot_Models_CVPR_2022_paper.pdf},}
@article{YadlowskyNaBaDuTi22,title={Bounds on the Conditional and Average Treatment Effect
with Unobserved Confounding Factors},author={Yadlowsky, Steve and Namkoong, Hongseok and Basu, Sanjay and Duchi, John and Tian, Lu},journal={Annals of Statistics},volume={50},number={5},pages={2587--2615},year={2022},url={https://projecteuclid.org/journals/annals-of-statistics/volume-50/issue-5/Bounds-on-the-conditional-and-average-treatment-effect-with-unobserved/10.1214/22-AOS2195.full},slide={YadlowskyNaBaDuTi22-slides.pdf}}
@inproceedings{NamkoongKeYaBr20,title={Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding},author={Namkoong$*$, Hongseok and Keramati$*$, Ramtin and Yadlowsky$*$, Steve and Brunskill, Emma},booktitle={Advances in Neural Information Processing Systems 33},year={2020},url={https://proceedings.neurips.cc/paper/2020/file/da21bae82c02d1e2b8168d57cd3fbab7-Paper.pdf},slide={YadlowskyNaBaDuTi22-slides.pdf}}
@article{DuchiHaNa22,author={Duchi, John C. and Hashimoto, Tatsunori and Namkoong, Hongseok},title={Distributionally Robust Losses Against Mixture Covariate Shifts},year={2022},journal={Operations Research},url={https://pubsonline.informs.org/doi/10.1287/opre.2022.2363},}
@article{DuchiNa21,author={Duchi, John C. and Namkoong, Hongseok},title={Learning Models with Uniform Performance via Distributionally
Robust Optimization},year={2021},volume={49},number={3},pages={1378-1406},journal={Annals of Statistics},url={https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-3/Learning-models-with-uniform-performance-via-distributionally-robust-optimization/10.1214/20-AOS2004.full},}
@article{DuchiGlNa21,title={Statistics of Robust Optimization: A Generalized Empirical
Likelihood Approach},author={Duchi, John C. and Glynn, Peter W. and Namkoong, Hongseok},year={2021},volume={46},number={3},pages={946-969},journal={Mathematics of Operations Research},note={APS Best Student Paper Prize},url={https://pubsonline.informs.org/doi/10.1287/moor.2020.1085},}
@inproceedings{SinhaNaVoDu18,title={Certifiable Distributional Robustness with Principled Adversarial Training},author={Sinha$*$, Aman and Namkoong$*$, Hongseok and Volpi, Riccardo and Duchi, John},booktitle={International Conference on Learning Representations},year={2018},note={Selected for a full oral presentation; 2\% of submissions},url={https://arxiv.org/abs/1710.10571},}
@article{DuchiNa19,title={Variance-based regularization with convex objectives},author={Duchi, John C. and Namkoong, Hongseok},year={2019},journal={Journal of Machine Learning Research},note={Conference version won NeurIPS 2017 Best Paper Award},url={https://jmlr.csail.mit.edu/papers/volume20/17-750/17-750.pdf},slide={NamkoongDu17-slides.pdf},}
@inproceedings{VolpiNaSeDuMuSa18,title={Generalizing to Unseen Domains via Adversarial Data Augmentation},author={Volpi$*$, Riccardo and Namkoong$*$, Hongseok and Duchi, John and Murino, Vittorio and Savarese, Silvio},booktitle={Advances in Neural Information Processing Systems 31},year={2018},url={https://proceedings.neurips.cc/paper_files/paper/2018/file/1d94108e907bb8311d8802b48fd54b4a-Paper.pdf},}
@inproceedings{OKellySiNaDuTe18,title={Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation},author={O'Kelly$*$, Mathew and Sinha$*$, Aman and Namkoong$*$, Hongseok and Duchi, John and Tedrake, Russ},booktitle={Advances in Neural Information Processing Systems 31},year={2018},url={https://proceedings.neurips.cc/paper_files/paper/2018/file/653c579e3f9ba5c03f2f2f8cf4512b39-Paper.pdf},}
@inproceedings{HashimotoSrNaLi18,title={Fairness Without Demographics in Repeated Loss Minimization},author={Hashimoto, Tatsunori and Srivastava, Megha and Namkoong, Hongseok and Liang, Percy},booktitle={International Conference on Machine Learning},year={2018},note={Best Paper Runner-up Award},url={https://proceedings.mlr.press/v80/hashimoto18a/hashimoto18a.pdf},}
@inproceedings{NamkoongSiYaDu17,title={Adaptive sampling probabilities for non-smooth optimization},author={Namkoong, Hongseok and Sinha, Aman and Yadlowsky, Steve and Duchi, John C},booktitle={International Conference on Machine Learning},pages={2574--2583},year={2017},url={https://proceedings.mlr.press/v70/namkoong17a/namkoong17a.pdf},}
@inproceedings{NamkoongDu16,author={Namkoong, Hongseok and Duchi, John C.},title={Stochastic Gradient Methods for Distributionally
Robust Optimization with $f$-divergences},year={2016},booktitle={Advances in Neural Information Processing Systems 29},url={https://papers.nips.cc/paper_files/paper/2016/hash/4588e674d3f0faf985047d4c3f13ed0d-Abstract.html},slide={NamkoongDu16-slides.pdf}}