Hongseok Namkoong

namkoong@gsb.columbia.edu

I am an assistant professor in the Decision, Risk, and Operations division at Columbia Business School and a member of the Data Science Institute. My research interests lie at the interface of machine learning and decision-making. I develop robust and reliable methods for data-driven decision making, extending and connecting tools across ML, operations research, and causal inference. Outside of academia, I serve as a LinkedIn Scholar at LinkedIn’s Trust and Responsible AI team.

I received my Ph.D. from Stanford University and spent a year at Meta’s Adaptive Experimentation team as a research scientist. Here’s a more formal bio in the third person.

I go by Hong; alternatively, here’s a link the correct pronunciation of my first name.

news

Jun 10, 2024	In this significantly updated paper, we use rigorous and extensive empirical analysis to highlight the shortcomings of robust learning algorithms and advocate for new data-driven inductive paradigm for algorithm development. Also check out our NeurIPS 2023 tutorial on distribution shifts and my DRO brown bag overviewing my group’s work in this direction.
Dec 15, 2023	Watch my SNAPP talk on Adaptive Experimentation at Scale.
May 15, 2023	Watch my talk on Robust Causal Inference that I gave at the IFDS workshop on distributional robustness in data science.

selected publications

AI-driven Decisions

Differentiable Discrete Event Simulation for Queuing Network Control

Ethan Che, Jing Dong, and Hongseok Namkoong

arXiv:2409.03740 [cs.LG], 2024

arXiv Bib

@article{CheDoNa24,
  title = {Differentiable Discrete Event Simulation for Queuing Network Control},
  author = {Che, Ethan and Dong, Jing and Namkoong, Hongseok},
  journal = {arXiv:2409.03740 [cs.LG]},
  year = {2024},
  url = {https://arxiv.org/abs/2409.03740},
}

AI-driven Decisions

Optimization-Driven Adaptive Experimentation

Ethan Che, Daniel Jiang, Hongseok Namkoong , and Jimmy Wang

arXiv:2408.04570 [cs.LG], 2024

Selected for oral presentations at the Econometric Society Interdisciplinary Frontiers: Economics and AI+ML conference and Conference on Digital Experimentation

arXiv Bib Code Slides

@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},
}

AI-driven Decisions

Posterior Sampling via Autoregressive Generation

Kelly Zhang*, Tiffany Cai*, Hongseok Namkoong, and Daniel Russo

arXiv:2405.19466 [cs.LG], 2024

Selected for presentation at the Econometric Society Interdisciplinary Frontiers: Economics and AI+ML conference

arXiv Bib Slides

@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},
}

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

Jiashuo Liu*, Tianyu Wang*, Peng Cui, and Hongseok Namkoong

arXiv:2307.05284 [cs.LG], 2024

Conference version appeared in NeurIPS 2023.

Abs arXiv Bib Video Slides Website

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.}, }