Henry Lam

Associate Professor@Columbia University·OpenReview

研究方向

Monte Carlo methods · resampling · uncertainty quantification · robust optimization · prescriptive analytics · variance reduction · data-driven optimization

7.8

Subsampled Ensemble Can Improve Generalization Tail Exponentially

NeurIPS 2025Poster

三作

6.5

MallowsPO: Fine-Tune Your LLM with Preference Dispersions

ICLR 2025Poster

三作

6.4

The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective

NeurIPS 2025Poster

5.8

Subsampled Ensemble Can Improve Generalization Tail Exponentially

ICLR 2025Rejected

三作

4.6

LLM Embeddings Improve Test-Time Adaptation to Tabular $Y|X$-Shifts

ICLR 2025Rejected

三作

7.0

Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?

NeurIPS 2024Poster

二作

6.8

Learning from Sparse Offline Datasets via Conservative Density Estimation

合作者 (20)