Kwai Hung Henry Lam

23 catalog papers. Identity-linked publications and additional name matches are distinguished below.

ORCID: 0000-0002-3193-563X

Department of Industrial Engineering and Operations Research, Columbia University, New York, NY10027, USA

Publications

• Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent — Journal of Machine Learning Research 2026 (name-matched, not ORCID-linked)
• Convolution Bounds on Quantile Aggregation — Operations Research 2025
• Distributionally Constrained Black-Box Stochastic Gradient Estimation and Optimization — Operations Research 2025
• Uncertainty Quantification and Exploration for Reinforcement Learning — Operations Research 2024
• Efficient Learning for Clustering and Optimizing Context-Dependent Designs — Operations Research 2024
• A Shrinkage Approach to Improve Direct Bootstrap Resampling Under Input Uncertainty — INFORMS Journal on Computing 2024
• Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation — Operations Research 2024
• Higher-order coverage errors of batching methods via Edgeworth expansions on t-statistics — The Annals of Statistics 2024
• Overconservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation — Management Science 2024
• Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective — Operations Research 2023
• Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence — Journal of Machine Learning Research 2023 (name-matched, not ORCID-linked)
• Subsampling to Enhance Efficiency in Input Uncertainty Quantification — Operations Research 2022
• A New Likelihood Ratio Method for Training Artificial Neural Networks — INFORMS Journal on Computing 2022
• Learning-Based Robust Optimization: Procedures and Statistical Guarantees — Management Science 2021
• Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling — Operations Research 2020
• Optimization-Based Calibration of Simulation Input Models — Operations Research 2019
• Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees — Operations Research 2019
• Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization — Operations Research 2019
• Sensitivity to Serial Dependency of Input Processes: A Robust Approach — Management Science 2018
• Tail Analysis Without Parametric Models: A Worst-Case Perspective — Operations Research 2017
• Certifiable Deep Importance Sampling for Rare-Event Simulation of Black Box Systems — Operations Research
• On the Impossibility of Statistically Improving Empirical Optimization: A Second Order Stochastic Dominance Perspective — Management Science
• A Distributionally Robust Optimization Framework for Extreme Event Estimation — Journal of the American Statistical Association