Rare event simulation for a slotted time M/G/s model. - Jose H. Blanchet; Peter W. Glynn; Henry Lam (Queueing Syst. Theory Appl., 2009)

Spectral Library Searching for Peptide Identification via Tandem MS. - Henry Lam; Ruedi Aebersold (Proteome Bioinformatics, 2010)

Exact asymptotic for infinite-server queues. - Henry Lam (QTNA, 2011)

Rare event simulation techniques. - Jose H. Blanchet; Henry Lam (WSC, 2011)

Importance sampling for actuarial cost analysis under a heavy traffic model. - Jose H. Blanchet; Henry Lam (WSC, 2011)

Information Dissemination via Random Walks in d-Dimensional Space - Henry Lam; Zhenming Liu; Michael Mitzenmacher; Xiaorui Sun; Yajun Wang 001 (CoRR, 2011)

Information dissemination via random walks in d-dimensional space. - Henry Lam; Zhenming Liu; Michael Mitzenmacher; Xiaorui Sun; Yajun Wang 001 (SODA, 2012)

Chernoff-Hoeffding Bounds for Markov Chains: Generalized and Simplified. - Kai-Min Chung; Henry Lam; Zhenming Liu; Michael Mitzenmacher (STACS, 2012)

Efficient importance sampling under partial information. - Henry Lam (WSC, 2012)

Why Steiner-tree type algorithms work for community detection. - Mung Chiang; Henry Lam; Zhenming Liu; H. Vincent Poor (AISTATS, 2013)

Iterative methods for robust estimation under bivariate distributional uncertainty. - Henry Lam; Soumyadip Ghosh (WSC, 2013)

Graph-based peak alignment algorithms for multiple liquid chromatography-mass spectrometry datasets. - Jijie Wang; Henry Lam (Bioinform., 2013)

A Bayesian Framework for Online Classifier Ensemble. - Qinxun Bai; Henry Lam; Stan Sclaroff (ICML, 2014)

Robust rare-event performance analysis with natural non-convex constraints. - Jose H. Blanchet; Christopher Dolan; Henry Lam (WSC, 2014)

Reconstructing input models via simulation optimization. - Aleksandrina Goeva; Henry Lam; Bo Zhang (WSC, 2014)

From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments. - Henry Lam; Zhenming Liu (CoRR, 2014)

Rare-Event Simulation for Many-Server Queues. - Jose H. Blanchet; Henry Lam (Math. Oper. Res., 2014)

Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model. - Christopher G. Brinton; Mung Chiang; Shaili Jain; Henry Lam; Zhenming Liu; Felix Ming Fai Wong (IEEE Trans. Learn. Technol., 2014)

Mirror descent stochastic approximation for computing worst-case stochastic input models. - Soumyadip Ghosh; Henry Lam (WSC, 2015)

A statistical perspective on linear programs with uncertain parameters. - L. Jeff Hong; Henry Lam (WSC, 2015)

Simulating tail events with unspecified tail models. - Henry Lam; Clementine Mottet (WSC, 2015)

Quantifying uncertainty in sample average approximation. - Henry Lam; Enlu Zhou (WSC, 2015)

A Bayesian Approach for Online Classifier Ensemble. - Qinxun Bai; Henry Lam; Stan Sclaroff (CoRR, 2015)

Approximating data-driven joint chance-constrained programs via uncertainty set construction. - L. Jeff Hong; Zhiyuan Huang; Henry Lam (WSC, 2016)

Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation. - Henry Lam (WSC, 2016)

The empirical likelihood approach to simulation input uncertainty. - Henry Lam; Huajie Qian (WSC, 2016)

Learning stochastic model discrepancy. - Matthew Plumlee; Henry Lam (WSC, 2016)

Accelerated Evaluation of Automated Vehicles using Piecewise Mixture Distribution Models. - Zhiyuan Huang; Ding Zhao; Henry Lam; David J. LeBlanc; Huei Peng (CoRR, 2016)

Accelerated Evaluation of Automated Vehicles based on Importance Sampling Techniques. - Ding Zhao; Henry Lam; Huei Peng; Shan Bao; David J. LeBlanc; Kazutoshi Nobukawa; Christopher S. Pan (CoRR, 2016)

Robust Sensitivity Analysis for Stochastic Systems. - Henry Lam (Math. Oper. Res., 2016)

Evaluation of automated vehicles in the frontal cut-in scenario - An enhanced approach using piecewise mixture models. - Zhiyuan Huang; Ding Zhao; Henry Lam; David J. LeBlanc; Huei Peng (ICRA, 2017)

Towards affordable on-track testing for autonomous vehicle - A Kriging-based statistical approach. - Zhiyuan Huang; Henry Lam; Ding Zhao (ITSC, 2017)

An accelerated testing approach for automated vehicles with background traffic described by joint distributions. - Zhiyuan Huang; Henry Lam; Ding Zhao (ITSC, 2017)

Computing worst-case expectations given marginals via simulation. - Jose H. Blanchet; Fei He; Henry Lam (WSC, 2017)

Sequential experimentation to efficiently test automated vehicles. - Zhiyuan Huang; Henry Lam; Ding Zhao (WSC, 2017)

Improving prediction from stochastic simulation via model discrepancy learning. - Henry Lam; Xinyu Zhang; Matthew Plumlee (WSC, 2017)

Uncertainty quantification on simulation analysis driven by random forests. - Amirhossein Meisami; Mark P. Van Oyen; Henry Lam (WSC, 2017)

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods. - Zhiyuan Huang; Yaohui Guo; Henry Lam; Ding Zhao (CoRR, 2017)

Tail Analysis Without Parametric Models: A Worst-Case Perspective. - Henry Lam; Clementine Mottet (Oper. Res., 2017)

The empirical likelihood approach to quantifying uncertainty in sample average approximation. - Henry Lam; Enlu Zhou (Oper. Res. Lett., 2017)

Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques. - Ding Zhao; Henry Lam; Huei Peng; Shan Bao; David J. LeBlanc; Kazutoshi Nobukawa; Christopher S. Pan (IEEE Trans. Intell. Transp. Syst., 2017)

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods. - Zhiyuan Huang; Yaohui Guo; Mansur Arief; Henry Lam; Ding Zhao (ACC, 2018)

Synthesis of Different Autonomous Vehicles Test Approaches. - Zhiyuan Huang; Mansur Arief; Henry Lam; Ding Zhao (ITSC, 2018)

Sequential Learning under Probabilistic Constraints. - Amirhossein Meisami; Henry Lam; Chen Dong 004; Abhishek Pani (UAI, 2018)

Revisiting Direct bootstrap resampling for input Model uncertainty. - Russell R. Barton; Henry Lam; Eunhye Song (WSC, 2018)

Achieving Optimal Bias-variance Tradeoff in Online derivative estimation. - Thibault Duplay; Henry Lam; Xinyu Zhang (WSC, 2018)

Constructing simulation output Intervals under input uncertainty via Data sectioning. - Peter W. Glynn; Henry Lam (WSC, 2018)

Designing Importance samplers to simulate Machine Learning Predictors via Optimization. - Zhiyuan Huang; Henry Lam; Ding Zhao (WSC, 2018)

Rare-Event simulation without Structural Information: a Learning-based Approach. - Zhiyuan Huang; Henry Lam; Ding Zhao (WSC, 2018)

On efficiencies of stochastic Optimization Procedures under Importance Sampling. - Henry Lam; Guangxin Jiang; Michael C. Fu 001 (WSC, 2018)

Sampling uncertain Constraints under parametric distributions. - Henry Lam; Fengpei Li (WSC, 2018)

Subsampling variance for input uncertainty Quantification. - Henry Lam; Huajie Qian (WSC, 2018)

Assessing solution Quality in stochastic Optimization via bootstrap Aggregating. - Henry Lam; Huajie Qian (WSC, 2018)

Robust and parallel Bayesian model selection. - Michael Minyi Zhang; Henry Lam; Lizhen Lin (Comput. Stat. Data Anal., 2018)

Sensitivity to Serial Dependency of Input Processes: A Robust Approach. - Henry Lam (Manag. Sci., 2018)

Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models. - Zhiyuan Huang; Henry Lam; David J. LeBlanc; Ding Zhao (IEEE Trans. Intell. Transp. Syst., 2018)

Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers. - Ding Zhao; Xianan Huang; Huei Peng; Henry Lam; David J. LeBlanc (IEEE Trans. Intell. Transp. Syst., 2018)

Evaluation Uncertainty in Data-Driven Self-Driving Testing. - Zhiyuan Huang; Mansur Arief; Henry Lam; Ding Zhao (ITSC, 2019)

On The Impacts of Tail Model Uncertainty in Rare-Event Estimation. - Zhiyuan Huang; Henry Lam (WSC, 2019)

Random Perturbation and Bagging to Quantify Input Uncertainty. - Henry Lam; Huajie Qian (WSC, 2019)

Validating Optimization with Uncertain Constraints. - Henry Lam; Huajie Qian (WSC, 2019)

On The Stability of Kernelized Control Functionals On Partial And Biased Stochastic Inputs. - Henry Lam; Haofeng Zhang 002 (WSC, 2019)

Minimax Efficient Finite-Difference Gradient Estimators. - Henry Lam; Xuhui Zhang (WSC, 2019)

From Data to Stochastic Modeling and Decision Making: What Can We Do Better? - Joost Berkhout; Bernd Heidergott; Henry Lam; Yijie Peng (Asia Pac. J. Oper. Res., 2019)

Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation. - Zhiyuan Huang; Mansur Arief; Henry Lam; Ding Zhao (CoRR, 2019)

Efficient Inference and Exploration for Reinforcement Learning. - Yi Zhu; Jing Dong; Henry Lam (CoRR, 2019)

Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees. - Soumyadip Ghosh; Henry Lam (Oper. Res., 2019)

Optimization-Based Calibration of Simulation Input Models. - Aleksandrina Goeva; Henry Lam; Huajie Qian; Bo Zhang (Oper. Res., 2019)

Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization. - Henry Lam (Oper. Res., 2019)

Constrained Reinforcement Learning via Policy Splitting. - Haoxian Chen 002; Henry Lam; Fengpei Li; Amirhossein Meisami (ACML, 2020)

Robust Importance Weighting for Covariate Shift. - Fengpei Li; Henry Lam; Siddharth Prusty (AISTATS, 2020)

On the Error of Naive Rare-Event Monte Carlo Estimator. - Yuanlu Bai; Henry Lam (WSC, 2020)

Calibrating Input Parameters via Eligibility Sets. - Yuanlu Bai; Henry Lam (WSC, 2020)

Distributionally Constrained Stochastic Gradient Estimation Using Noisy Function Evaluations. - Henry Lam; Junhui Zhang (WSC, 2020)

Optimally Tuning Finite-Difference Estimators. - Haidong Li; Henry Lam (WSC, 2020)

Context-Dependent Ranking and Selection under a Bayesian Framework. - Haidong Li; Henry Lam; Zhe Liang; Yijie Peng (WSC, 2020)

Sample Average Approximation For Functional Decisions Under Shape Constraints. - Dashi I. Singham; Henry Lam (WSC, 2020)

Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy. - Mansur Arief; Zhiyuan Huang; Guru Koushik Senthil Kumar; Yuanlu Bai; Shengyi He; Wenhao Ding; Henry Lam; Ding Zhao (CoRR, 2020)

Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling. - Yijie Peng; Michael C. Fu 001; Bernd Heidergott; Henry Lam (Oper. Res., 2020)

Parametric Scenario Optimization under Limited Data: A Distributionally Robust Optimization View. - Henry Lam; Fengpei Li (ACM Trans. Model. Comput. Simul., 2020)

Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems. - Mansur Arief; Zhiyuan Huang; Guru Koushik Senthil Kumar; Yuanlu Bai; Shengyi He; Wenhao Ding; Henry Lam; Ding Zhao (AISTATS, 2021)

Learning Prediction Intervals for Regression: Generalization and Calibration. - Haoxian Chen 002; Ziyi Huang; Henry Lam; Huajie Qian; Haofeng Zhang 002 (AISTATS, 2021)

Higher-Order Coverage Error Analysis for Batching and Sectioning. - Shengyi He; Henry Lam (WSC, 2021)

Neural Predictive Intervals for Simulation Metamodeling. - Henry Lam; Haofeng Zhang 002 (WSC, 2021)

Simulating New York City Hospital Load Balancing During COVID-19. - Enrique Lelo de Larrea; Henry Lam; Elioth Sanabria; Jay Sethuraman; Sevin Mohammadi; Audrey Olivier; Andrew W. Smyth; Edward M. Dolan; Nicholas E. Johnson; Timothy R. Kepler; Afsan Quayyum; Kathleen S. Thomson (WSC, 2021)

Short-Term Adaptive Emergency Call Volume Prediction. - Elioth Sanabria; Henry Lam; Enrique Lelo de Larrea; Jay Sethuraman; Sevin Mohammadi; Audrey Olivier; Andrew W. Smyth; Edward M. Dolan; Nicholas E. Johnson; Timothy R. Kepler; Afsan Quayyum; Kathleen S. Thomson (WSC, 2021)

Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set. - Yuanlu Bai; Tucker Balch; Haoxian Chen 002; Danial Dervovic; Henry Lam; Svitlana Vyetrenko (CoRR, 2021)

Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling. - Mengdi Xu; Peide Huang; Fengpei Li; Jiacheng Zhu; Xuewei Qi; Kentaro Oguchi 001; Zhiyuan Huang; Henry Lam; Ding Zhao (CoRR, 2021)

Complexity-Free Generalization via Distributionally Robust Optimization. - Henry Lam; Yibo Zeng (CoRR, 2021)

Quantifying Epistemic Uncertainty in Deep Learning. - Ziyi Huang; Henry Lam; Haofeng Zhang 002 (CoRR, 2021)

Efficient Calibration of Multi-Agent Market Simulators from Time Series with Bayesian Optimization. - Yuanlu Bai; Henry Lam; Svitlana Vyetrenko; Tucker Balch (CoRR, 2021)

Learning-Based Robust Optimization: Procedures and Statistical Guarantees. - L. Jeff Hong; Zhiyuan Huang; Henry Lam (Manag. Sci., 2021)

Minimax efficient finite-difference stochastic gradient estimators using black-box function evaluations. - Henry Lam; Haidong Li; Xuhui Zhang (Oper. Res. Lett., 2021)

Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization. - Yuanlu Bai; Henry Lam; Tucker Balch; Svitlana Vyetrenko (ICAIF, 2022)

Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling. - Mengdi Xu; Peide Huang; Fengpei Li; Jiacheng Zhu; Xuewei Qi; Kentaro Oguchi 001; Zhiyuan Huang; Henry Lam; Ding Zhao (IROS, 2022)

Certifiable Evaluation for Autonomous Vehicle Perception Systems using Deep Importance Sampling (Deep IS). - Mansur Arief; Zhepeng Cen; Zhenyuan Liu; Zhiyuan Huang; Bo Li 026; Henry Lam; Ding Zhao (ITSC, 2022)

Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization. - Yibo Zeng; Henry Lam (NeurIPS, 2022)

Importance Sampling for Rare-Event Gradient Estimation. - Yuanlu Bai; Shengyi He; Henry Lam; Guangxin Jiang; Michael C. Fu 001 (WSC, 2022)

Rare-Event Simulation Without Variance Reduction: An Extreme Value Theory Approach. - Yuanlu Bai; Henry Lam; Sebastian Engelke (WSC, 2022)

Distributional Input Uncertainty. - Motong Chen; Zhenyuan Liu; Henry Lam (WSC, 2022)

Batching on Biased Estimators. - Shengyi He; Henry Lam (WSC, 2022)

Cheap Bootstrap for Input Uncertainty Quantification. - Henry Lam (WSC, 2022)

Generalized Bayesian Upper Confidence Bound with Approximate Inference for Bandit Problems. - Ziyi Huang; Henry Lam; Amirhossein Meisami; Haofeng Zhang 002 (CoRR, 2022)

Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling. - Mansur Arief; Zhepeng Cen; Zhenyuan Liu; Zhiyuan Huang; Henry Lam; Bo Li 026; Ding Zhao (CoRR, 2022)

Evaluating Aleatoric Uncertainty via Conditional Generative Models. - Ziyi Huang; Henry Lam; Haofeng Zhang 002 (CoRR, 2022)

Adaptive Data Fusion for Multi-task Non-smooth Optimization. - Henry Lam; Kaizheng Wang; Yuhang Wu; Yichen Zhang (CoRR, 2022)

A New Likelihood Ratio Method for Training Artificial Neural Networks. - Yijie Peng; Li Xiao 005; Bernd Heidergott; L. Jeff Hong; Henry Lam (INFORMS J. Comput., 2022)

Subsampling to Enhance Efficiency in Input Uncertainty Quantification. - Henry Lam; Huajie Qian (Oper. Res., 2022)

Rare-event Simulation for Neural Network and Random Forest Predictors. - Yuanlu Bai; Zhiyuan Huang; Henry Lam; Ding Zhao (ACM Trans. Model. Comput. Simul., 2022)

General Feasibility Bounds for Sample Average Approximation via Vapnik-Chervonenkis Dimension. - Henry Lam; Fengpei Li (SIAM J. Optim., 2022)

Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation. - Garud Iyengar; Henry Lam; Tianyu Wang (AISTATS, 2023)

Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables. - Mengdi Xu; Peide Huang; Yaru Niu; Visak Kumar; Jielin Qiu; Chao Fang; Kuan-Hui Lee; Xuewei Qi; Henry Lam; Bo Li 026; Ding Zhao (AISTATS, 2023)

Bootstrap in High Dimension with Low Computation. - Henry Lam; Zhenyuan Liu (ICML, 2023)

Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework. - Ziyi Huang; Henry Lam; Amirhossein Meisami; Haofeng Zhang 002 (NeurIPS, 2023)

Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks. - Ziyi Huang; Henry Lam; Haofeng Zhang 002 (NeurIPS, 2023)

Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics. - Yu Chen; Fengpei Li; Anderson Schneider; Yuriy Nevmyvaka; Asohan Amarasingham; Henry Lam (UAI, 2023)

Curse of Dimensionality in Rare-Event Simulation. - Yuanlu Bai; Antonius B. Dieker; Henry Lam (WSC, 2023)

Optimal Batching Under Computation Budget. - Shengyi He; Henry Lam (WSC, 2023)

Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty Applications. - Henry Lam (WSC, 2023)

Resampling Stochastic Gradient Descent Cheaply. - Henry Lam; Zitong Wang 005 (WSC, 2023)

Estimate-Then-Optimize Versus Integrated-Estimation-Optimization: A Stochastic Dominance Perspective. - Adam N. Elmachtoub; Henry Lam; Haofeng Zhang 002; Yunfan Zhao (CoRR, 2023)

Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes. - Yu Chen; Fengpei Li; Anderson Schneider; Yuriy Nevmyvaka; Asohan Amarasingham; Henry Lam (CoRR, 2023)

Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization. - Garud Iyengar; Henry Lam; Tianyu Wang (CoRR, 2023)

Smoothed f-Divergence Distributionally Robust Optimization: Exponential Rate Efficiency and Complexity-Free Calibration. - Zhenyuan Liu; Bart P. G. Van Parys; Henry Lam (CoRR, 2023)

Pseudo-Bayesian Optimization. - Haoxian Chen 002; Henry Lam (CoRR, 2023)

Resampling Stochastic Gradient Descent Cheaply for Efficient Uncertainty Quantification. - Henry Lam; Zitong Wang 005 (CoRR, 2023)

Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective. - Henry Lam; Xinyu Zhang; Xuhui Zhang (Oper. Res., 2023)

Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence. - Henry Lam; Haofeng Zhang 002 (J. Mach. Learn. Res., 2023)

Learning from Sparse Offline Datasets via Conservative Density Estimation. - Zhepeng Cen; Zuxin Liu; Zitong Wang 005; Yihang Yao; Henry Lam; Ding Zhao (ICLR, 2024)

Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? - Garud Iyengar; Henry Lam; Tianyu Wang (NeurIPS, 2024)

Importance Sampling of Rare Events for Distribution Networks with Stochastic Loads. - Mark Christianen; Henry Lam; Maria Vlasiou; Bert Zwart (WSC, 2024)

Data-Driven Solutions and Uncertainty Quantification for Multistage Stochastic Optimization. - Yunhao Yan; Henry Lam (WSC, 2024)

Bagging Improves Generalization Exponentially. - Huajie Qian; Donghao Ying; Henry Lam; Wotao Yin (CoRR, 2024)

Mallows-DPO: Fine-Tune Your LLM with Preference Dispersions. - Haoxian Chen 002; Hanyang Zhao; Henry Lam; David D. Yao; Wenpin Tang (CoRR, 2024)

Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits. - Ziyi Huang; Henry Lam; Haofeng Zhang 002 (CoRR, 2024)

LLM Embeddings Improve Test-time Adaptation to Tabular Y|X-Shifts. - Yibo Zeng; Jiashuo Liu; Henry Lam; Hongseok Namkoong (CoRR, 2024)

Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate. - Fengpei Li; Haoxian Chen 002; Jiahe Lin; Arkin Gupta; Xiaowei Tan; Gang Xu; Yuriy Nevmyvaka; Agostino Capponi; Henry Lam (CoRR, 2024)

A Shrinkage Approach to Improve Direct Bootstrap Resampling Under Input Uncertainty. - Eunhye Song; Henry Lam; Russell R. Barton (INFORMS J. Comput., 2024)

Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation. - Shengyi He; Guangxin Jiang; Henry Lam; Michael C. Fu 001 (Oper. Res., 2024)

Efficient Learning for Clustering and Optimizing Context-Dependent Designs. - Haidong Li; Henry Lam; Yijie Peng (Oper. Res., 2024)

Uncertainty Quantification and Exploration for Reinforcement Learning. - Yi Zhu; Jing Dong 007; Henry Lam (Oper. Res., 2024)

Overconservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation. - Yuanlu Bai; Zhiyuan Huang; Henry Lam; Ding Zhao (Manag. Sci., 2024)

Burn-in selection in simulating stationary time series. - Yuanbo Li; Chu Kin Chan; Chun Yip Yau; Wai Leong Ng; Henry Lam (Comput. Stat. Data Anal., 2024)

Dissecting the Impact of Model Misspecification in Data-Driven Optimization. - Adam N. Elmachtoub; Henry Lam; Haixiang Lan; Haofeng Zhang 002 (AISTATS, 2025)

MallowsPO: Fine-Tune Your LLM with Preference Dispersions. - Haoxian Chen 002; Hanyang Zhao; Henry Lam; David D. Yao; Wenpin Tang (ICLR, 2025)

Uncertainty quantification and confidence intervals for naive rare-event estimators. - Yuanlu Bai; Henry Lam (J. Appl. Probab., 2025)

Stochastic Optimization with Optimal Importance Sampling. - Liviu Aolaritei; Bart P. G. Van Parys; Henry Lam; Michael I. Jordan (CoRR, 2025)

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning. - Jiashuo Liu; Tianyu Wang; Henry Lam; Hongseok Namkoong; Jose H. Blanchet (CoRR, 2025)