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2020 – today
- 2024
[j31]Yuanbo Li, Chu Kin Chan, Chun Yip Yau, Wai Leong Ng
, Henry Lam:
Burn-in selection in simulating stationary time series. Comput. Stat. Data Anal. 192: 107886 (2024)- [j30]Eunhye Song, Henry Lam, Russell R. Barton:
A Shrinkage Approach to Improve Direct Bootstrap Resampling Under Input Uncertainty. INFORMS J. Comput. 36(4): 1023-1039 (2024) - [j29]Haidong Li, Henry Lam, Yijie Peng:
Efficient Learning for Clustering and Optimizing Context-Dependent Designs. Oper. Res. 72(2): 617-638 (2024) - [j28]Yi Zhu, Jing Dong, Henry Lam:
Uncertainty Quantification and Exploration for Reinforcement Learning. Oper. Res. 72(4): 1689-1709 (2024) - [j27]Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao:
Overconservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation. Manag. Sci. 70(10): 6852-6873 (2024) - [c78]Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao:
Learning from Sparse Offline Datasets via Conservative Density Estimation. ICLR 2024 - [i41]Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao:
Learning from Sparse Offline Datasets via Conservative Density Estimation. CoRR abs/2401.08819 (2024) - [i40]Huajie Qian, Donghao Ying, Henry Lam, Wotao Yin:
Bagging Improves Generalization Exponentially. CoRR abs/2405.14741 (2024) - [i39]Haoxian Chen, Hanyang Zhao, Henry Lam, David D. Yao, Wenpin Tang:
Mallows-DPO: Fine-Tune Your LLM with Preference Dispersions. CoRR abs/2405.14953 (2024) - [i38]Ziyi Huang, Henry Lam, Haofeng Zhang:
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits. CoRR abs/2406.14071 (2024) - [i37]Yibo Zeng, Jiashuo Liu, Henry Lam, Hongseok Namkoong:
LLM Embeddings Improve Test-time Adaptation to Tabular Y|X-Shifts. CoRR abs/2410.07395 (2024) - 2023
- [j26]Henry Lam, Xinyu Zhang, Xuhui Zhang:
Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective. Oper. Res. 71(6): 2352-2373 (2023) - [j25]Henry Lam, Haofeng Zhang:
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence. J. Mach. Learn. Res. 24: 85:1-85:58 (2023) - [c77]Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao:
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables. AISTATS 2023: 2677-2703 - [c76]Garud Iyengar, Henry Lam, Tianyu Wang:
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation. AISTATS 2023: 9976-10011 - [c75]Henry Lam, Zhenyuan Liu:
Bootstrap in High Dimension with Low Computation. ICML 2023: 18419-18453 - [c74]Ziyi Huang, Henry Lam, Amirhossein Meisami, Haofeng Zhang:
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework. NeurIPS 2023 - [c73]Ziyi Huang, Henry Lam, Haofeng Zhang:
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks. NeurIPS 2023 - [c72]Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam:
Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics. UAI 2023: 369-380 - [c71]Yuanlu Bai, Antonius B. Dieker, Henry Lam:
Curse of Dimensionality in Rare-Event Simulation. WSC 2023: 375-384 - [c70]Shengyi He, Henry Lam:
Optimal Batching Under Computation Budget. WSC 2023: 433-444 - [c69]Henry Lam:
Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty Applications. WSC 2023: 1501-1515 - [c68]Henry Lam, Zitong Wang:
Resampling Stochastic Gradient Descent Cheaply. WSC 2023: 3681-3692 - [i36]Adam N. Elmachtoub, Henry Lam, Haofeng Zhang, Yunfan Zhao:
Estimate-Then-Optimize Versus Integrated-Estimation-Optimization: A Stochastic Dominance Perspective. CoRR abs/2304.06833 (2023) - [i35]Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam:
Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes. CoRR abs/2305.18412 (2023) - [i34]Ziyi Huang, Henry Lam, Haofeng Zhang:
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks. CoRR abs/2306.05674 (2023) - [i33]Garud Iyengar, Henry Lam, Tianyu Wang:
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization. CoRR abs/2306.10081 (2023) - [i32]Zhenyuan Liu, Bart P. G. Van Parys, Henry Lam:
Smoothed f-Divergence Distributionally Robust Optimization: Exponential Rate Efficiency and Complexity-Free Calibration. CoRR abs/2306.14041 (2023) - [i31]Haoxian Chen, Henry Lam:
Pseudo-Bayesian Optimization. CoRR abs/2310.09766 (2023) - [i30]Henry Lam, Zitong Wang:
Resampling Stochastic Gradient Descent Cheaply for Efficient Uncertainty Quantification. CoRR abs/2310.11065 (2023) - 2022
- [j24]Yijie Peng, Li Xiao, Bernd Heidergott, L. Jeff Hong, Henry Lam:
A New Likelihood Ratio Method for Training Artificial Neural Networks. INFORMS J. Comput. 34(1): 638-655 (2022) - [j23]Henry Lam, Huajie Qian:
Subsampling to Enhance Efficiency in Input Uncertainty Quantification. Oper. Res. 70(3): 1891-1913 (2022) - [j22]Henry Lam, Fengpei Li:
General Feasibility Bounds for Sample Average Approximation via Vapnik-Chervonenkis Dimension. SIAM J. Optim. 32(2): 1471-1497 (2022) - [j21]Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao:
Rare-event Simulation for Neural Network and Random Forest Predictors. ACM Trans. Model. Comput. Simul. 32(3): 18:1-18:33 (2022) - [c67]Yuanlu Bai, Henry Lam, Tucker Balch, Svitlana Vyetrenko:
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization. ICAIF 2022: 437-445 - [c66]Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao:
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling. IROS 2022: 12919-12926 - [c65]Mansur Arief, Zhepeng Cen, Zhenyuan Liu, Zhiyuan Huang, Bo Li, Henry Lam, Ding Zhao:
Certifiable Evaluation for Autonomous Vehicle Perception Systems using Deep Importance Sampling (Deep IS). ITSC 2022: 1736-1742 - [c64]Yibo Zeng, Henry Lam:
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization. NeurIPS 2022 - [c63]Yuanlu Bai, Henry Lam, Sebastian Engelke:
Rare-Event Simulation Without Variance Reduction: An Extreme Value Theory Approach. WSC 2022: 133-144 - [c62]Henry Lam:
Cheap Bootstrap for Input Uncertainty Quantification. WSC 2022: 2318-2329 - [c61]Shengyi He, Henry Lam:
Batching on Biased Estimators. WSC 2022: 2606-2616 - [c60]Motong Chen, Zhenyuan Liu, Henry Lam:
Distributional Input Uncertainty. WSC 2022: 2617-2628 - [c59]Yuanlu Bai, Shengyi He, Henry Lam, Guangxin Jiang, Michael C. Fu:
Importance Sampling for Rare-Event Gradient Estimation. WSC 2022: 3063-3074 - [i29]Ziyi Huang, Henry Lam, Amirhossein Meisami, Haofeng Zhang:
Generalized Bayesian Upper Confidence Bound with Approximate Inference for Bandit Problems. CoRR abs/2201.12955 (2022) - [i28]Mansur Arief, Zhepeng Cen, Zhenyuan Liu, Zhiyuan Huang, Henry Lam, Bo Li, Ding Zhao:
Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling. CoRR abs/2204.02351 (2022) - [i27]Ziyi Huang, Henry Lam, Haofeng Zhang:
Evaluating Aleatoric Uncertainty via Conditional Generative Models. CoRR abs/2206.04287 (2022) - [i26]Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao:
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables. CoRR abs/2210.12262 (2022) - [i25]Henry Lam, Kaizheng Wang, Yuhang Wu, Yichen Zhang:
Adaptive Data Fusion for Multi-task Non-smooth Optimization. CoRR abs/2210.12334 (2022) - [i24]Garud Iyengar, Henry Lam, Tianyu Wang:
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation. CoRR abs/2212.01518 (2022) - 2021
- [j20]L. Jeff Hong, Zhiyuan Huang, Henry Lam:
Learning-Based Robust Optimization: Procedures and Statistical Guarantees. Manag. Sci. 67(6): 3447-3467 (2021) - [j19]Henry Lam, Haidong Li, Xuhui Zhang:
Minimax efficient finite-difference stochastic gradient estimators using black-box function evaluations. Oper. Res. Lett. 49(1): 40-47 (2021) - [c58]Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao:
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems. AISTATS 2021: 595-603 - [c57]Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang:
Learning Prediction Intervals for Regression: Generalization and Calibration. AISTATS 2021: 820-828 - [c56]Shengyi He, Henry Lam:
Higher-Order Coverage Error Analysis for Batching and Sectioning. WSC 2021: 1-12 - [c55]Henry Lam, Haofeng Zhang:
Neural Predictive Intervals for Simulation Metamodeling. WSC 2021: 1-12 - [c54]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:
Simulating New York City Hospital Load Balancing During COVID-19. WSC 2021: 1-12 - [c53]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:
Short-Term Adaptive Emergency Call Volume Prediction. WSC 2021: 1-12 - [i23]Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang:
Learning Prediction Intervals for Regression: Generalization and Calibration. CoRR abs/2102.13625 (2021) - [i22]Yuanlu Bai, Tucker Balch, Haoxian Chen, Danial Dervovic, Henry Lam, Svitlana Vyetrenko:
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set. CoRR abs/2105.12893 (2021) - [i21]Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao:
Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling. CoRR abs/2106.10566 (2021) - [i20]Henry Lam, Yibo Zeng:
Complexity-Free Generalization via Distributionally Robust Optimization. CoRR abs/2106.11180 (2021) - [i19]Ziyi Huang, Henry Lam, Haofeng Zhang:
Quantifying Epistemic Uncertainty in Deep Learning. CoRR abs/2110.12122 (2021) - [i18]Yuanlu Bai, Henry Lam, Svitlana Vyetrenko, Tucker Balch:
Efficient Calibration of Multi-Agent Market Simulators from Time Series with Bayesian Optimization. CoRR abs/2112.03874 (2021) - 2020
- [j18]Yijie Peng, Michael C. Fu, Bernd Heidergott, Henry Lam:
Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling. Oper. Res. 68(6): 1896-1912 (2020) - [j17]Henry Lam, Fengpei Li:
Parametric Scenario Optimization under Limited Data: A Distributionally Robust Optimization View. ACM Trans. Model. Comput. Simul. 30(4): 21:1-21:41 (2020) - [c52]Haoxian Chen, Henry Lam, Fengpei Li, Amirhossein Meisami:
Constrained Reinforcement Learning via Policy Splitting. ACML 2020: 209-224 - [c51]Fengpei Li, Henry Lam, Siddharth Prusty:
Robust Importance Weighting for Covariate Shift. AISTATS 2020: 352-362 - [c50]Yuanlu Bai, Henry Lam:
On the Error of Naive Rare-Event Monte Carlo Estimator. WSC 2020: 397-408 - [c49]Henry Lam, Junhui Zhang:
Distributionally Constrained Stochastic Gradient Estimation Using Noisy Function Evaluations. WSC 2020: 445-456 - [c48]Haidong Li, Henry Lam:
Optimally Tuning Finite-Difference Estimators. WSC 2020: 457-468 - [c47]Haidong Li, Henry Lam, Zhe Liang, Yijie Peng:
Context-Dependent Ranking and Selection under a Bayesian Framework. WSC 2020: 2060-2070 - [c46]Yuanlu Bai, Henry Lam:
Calibrating Input Parameters via Eligibility Sets. WSC 2020: 2114-2125 - [c45]Dashi I. Singham, Henry Lam:
Sample Average Approximation For Functional Decisions Under Shape Constraints. WSC 2020: 2791-2799 - [i17]Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao:
Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy. CoRR abs/2006.15722 (2020) - [i16]Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao:
Rare-Event Simulation for Neural Network and Random Forest Predictors. CoRR abs/2010.04890 (2020)
2010 – 2019
- 2019
- [j16]Joost Berkhout, Bernd Heidergott, Henry Lam, Yijie Peng:
From Data to Stochastic Modeling and Decision Making: What Can We Do Better? Asia Pac. J. Oper. Res. 36(6): 1940012:1-1940012:20 (2019) - [j15]Soumyadip Ghosh, Henry Lam:
Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees. Oper. Res. 67(1): 232-249 (2019) - [j14]Henry Lam:
Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization. Oper. Res. 67(4): 1090-1105 (2019) - [j13]Aleksandrina Goeva, Henry Lam, Huajie Qian, Bo Zhang:
Optimization-Based Calibration of Simulation Input Models. Oper. Res. 67(5): 1362-1382 (2019) - [c44]Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao:
Evaluation Uncertainty in Data-Driven Self-Driving Testing. ITSC 2019: 1902-1907 - [c43]Henry Lam, Huajie Qian:
Random Perturbation and Bagging to Quantify Input Uncertainty. WSC 2019: 320-331 - [c42]Henry Lam, Haofeng Zhang:
On The Stability of Kernelized Control Functionals On Partial And Biased Stochastic Inputs. WSC 2019: 344-355 - [c41]Henry Lam, Xuhui Zhang:
Minimax Efficient Finite-Difference Gradient Estimators. WSC 2019: 392-403 - [c40]Zhiyuan Huang, Henry Lam:
On The Impacts of Tail Model Uncertainty in Rare-Event Estimation. WSC 2019: 950-961 - [c39]Henry Lam, Huajie Qian:
Validating Optimization with Uncertain Constraints. WSC 2019: 3621-3632 - [i15]Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao:
Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation. CoRR abs/1904.09306 (2019) - [i14]Yi Zhu, Jing Dong, Henry Lam:
Efficient Inference and Exploration for Reinforcement Learning. CoRR abs/1910.05471 (2019) - [i13]Henry Lam, Fengpei Li, Siddharth Prusty:
Robust Importance Weighting for Covariate Shift. CoRR abs/1910.06324 (2019) - 2018
- [j12]Michael Minyi Zhang, Henry Lam, Lizhen Lin:
Robust and parallel Bayesian model selection. Comput. Stat. Data Anal. 127: 229-247 (2018) - [j11]Henry Lam:
Sensitivity to Serial Dependency of Input Processes: A Robust Approach. Manag. Sci. 64(3): 1311-1327 (2018) - [j10]Ding Zhao, Xianan Huang, Huei Peng, Henry Lam, David J. LeBlanc:
Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers. IEEE Trans. Intell. Transp. Syst. 19(3): 733-744 (2018) - [j9]Zhiyuan Huang, Henry Lam, David J. LeBlanc, Ding Zhao:
Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models. IEEE Trans. Intell. Transp. Syst. 19(9): 2845-2855 (2018) - [c38]Zhiyuan Huang, Yaohui Guo, Mansur Arief, Henry Lam, Ding Zhao:
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods. ACC 2018: 4796-4802 - [c37]Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao:
Synthesis of Different Autonomous Vehicles Test Approaches. ITSC 2018: 2000-2005 - [c36]Amirhossein Meisami, Henry Lam, Chen Dong, Abhishek Pani:
Sequential Learning under Probabilistic Constraints. UAI 2018: 621-631 - [c35]Peter W. Glynn, Henry Lam:
Constructing simulation output Intervals under input uncertainty via Data sectioning. WSC 2018: 1551-1562 - [c34]Henry Lam, Huajie Qian:
Subsampling variance for input uncertainty Quantification. WSC 2018: 1611-1622 - [c33]Russell R. Barton, Henry Lam, Eunhye Song:
Revisiting Direct bootstrap resampling for input Model uncertainty. WSC 2018: 1635-1645 - [c32]Zhiyuan Huang, Henry Lam, Ding Zhao:
Designing Importance samplers to simulate Machine Learning Predictors via Optimization. WSC 2018: 1730-1741 - [c31]Zhiyuan Huang, Henry Lam, Ding Zhao:
Rare-Event simulation without Structural Information: a Learning-based Approach. WSC 2018: 1826-1837 - [c30]Thibault Duplay, Henry Lam, Xinyu Zhang:
Achieving Optimal Bias-variance Tradeoff in Online derivative estimation. WSC 2018: 1838-1849 - [c29]Henry Lam, Guangxin Jiang, Michael C. Fu:
On efficiencies of stochastic Optimization Procedures under Importance Sampling. WSC 2018: 1862-1873 - [c28]Henry Lam, Huajie Qian:
Assessing solution Quality in stochastic Optimization via bootstrap Aggregating. WSC 2018: 2061-2071 - [c27]Henry Lam, Fengpei Li:
Sampling uncertain Constraints under parametric distributions. WSC 2018: 2072-2083 - 2017
- [j8]Henry Lam, Clementine Mottet:
Tail Analysis Without Parametric Models: A Worst-Case Perspective. Oper. Res. 65(6): 1696-1711 (2017) - [j7]Henry Lam, Enlu Zhou:
The empirical likelihood approach to quantifying uncertainty in sample average approximation. Oper. Res. Lett. 45(4): 301-307 (2017) - [j6]Ding Zhao, Henry Lam, Huei Peng, Shan Bao, David J. LeBlanc, Kazutoshi Nobukawa, Christopher S. Pan:
Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques. IEEE Trans. Intell. Transp. Syst. 18(3): 595-607 (2017) - [c26]Zhiyuan Huang, Ding Zhao, Henry Lam, David J. LeBlanc, Huei Peng:
Evaluation of automated vehicles in the frontal cut-in scenario - An enhanced approach using piecewise mixture models. ICRA 2017: 197-202 - [c25]Zhiyuan Huang, Henry Lam, Ding Zhao:
Towards affordable on-track testing for autonomous vehicle - A Kriging-based statistical approach. ITSC 2017: 1-6 - [c24]Zhiyuan Huang, Henry Lam, Ding Zhao:
An accelerated testing approach for automated vehicles with background traffic described by joint distributions. ITSC 2017: 933-938 - [c23]Henry Lam, Xinyu Zhang, Matthew Plumlee:
Improving prediction from stochastic simulation via model discrepancy learning. WSC 2017: 1808-1819 - [c22]Jose H. Blanchet, Fei He, Henry Lam:
Computing worst-case expectations given marginals via simulation. WSC 2017: 2315-2323 - [c21]Zhiyuan Huang, Henry Lam, Ding Zhao:
Sequential experimentation to efficiently test automated vehicles. WSC 2017: 3078-3089 - [c20]Amirhossein Meisami, Mark P. Van Oyen, Henry Lam:
Uncertainty quantification on simulation analysis driven by random forests. WSC 2017: 3266-3274 - [i12]Zhiyuan Huang, Ding Zhao, Henry Lam, David J. LeBlanc:
Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models. CoRR abs/1701.08915 (2017) - [i11]Zhiyuan Huang, Henry Lam, Ding Zhao:
Sequential Experimentation to Efficiently Test Automated Vehicles. CoRR abs/1707.00224 (2017) - [i10]Zhiyuan Huang, Henry Lam, Ding Zhao:
An Accelerated Testing Approach for Automated Vehicles with Background Traffic Described by Joint Distributions. CoRR abs/1707.04896 (2017) - [i9]Zhiyuan Huang, Henry Lam, Ding Zhao:
Towards Affordable On-track Testing for Autonomous Vehicle - A Kriging-based Statistical Approach. CoRR abs/1707.04897 (2017) - [i8]Zhiyuan Huang, Yaohui Guo, Henry Lam, Ding Zhao:
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods. CoRR abs/1710.00283 (2017) - 2016
- [j5]Henry Lam:
Robust Sensitivity Analysis for Stochastic Systems. Math. Oper. Res. 41(4): 1248-1275 (2016) - [c19]Henry Lam:
Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation. WSC 2016: 178-192 - [c18]L. Jeff Hong, Zhiyuan Huang, Henry Lam:
Approximating data-driven joint chance-constrained programs via uncertainty set construction. WSC 2016: 389-400 - [c17]Matthew Plumlee, Henry Lam:
Learning stochastic model discrepancy. WSC 2016: 413-424 - [c16]Henry Lam, Huajie Qian:
The empirical likelihood approach to simulation input uncertainty. WSC 2016: 791-802 - [i7]Ding Zhao, Henry Lam, Huei Peng, Shan Bao, David J. LeBlanc, Kazutoshi Nobukawa, Christopher S. Pan:
Accelerated Evaluation of Automated Vehicles based on Importance Sampling Techniques. CoRR abs/1605.04965 (2016) - [i6]Ding Zhao, Xianan Huang, Huei Peng, Henry Lam, David J. LeBlanc:
Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers. CoRR abs/1607.02687 (2016) - [i5]Zhiyuan Huang, Ding Zhao, Henry Lam, David J. LeBlanc, Huei Peng:
Accelerated Evaluation of Automated Vehicles using Piecewise Mixture Distribution Models. CoRR abs/1610.09450 (2016) - 2015
- [c15]Henry Lam, Clementine Mottet:
Simulating tail events with unspecified tail models. WSC 2015: 392-402 - [c14]Soumyadip Ghosh, Henry Lam:
Mirror descent stochastic approximation for computing worst-case stochastic input models. WSC 2015: 425-436 - [c13]L. Jeff Hong, Henry Lam:
A statistical perspective on linear programs with uncertain parameters. WSC 2015: 3690-3701 - [c12]Henry Lam, Enlu Zhou:
Quantifying uncertainty in sample average approximation. WSC 2015: 3846-3857 - [i4]Qinxun Bai, Henry Lam, Stan Sclaroff:
A Bayesian Approach for Online Classifier Ensemble. CoRR abs/1507.02011 (2015) - 2014
- [j4]Jose H. Blanchet, Henry Lam:
Rare-Event Simulation for Many-Server Queues. Math. Oper. Res. 39(4): 1142-1178 (2014) - [j3]Christopher G. Brinton, Mung Chiang, Shaili Jain, Henry Lam, Zhenming Liu, Felix Ming Fai Wong:
Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model. IEEE Trans. Learn. Technol. 7(4): 346-359 (2014) - [c11]Qinxun Bai, Henry Lam, Stan Sclaroff:
A Bayesian Framework for Online Classifier Ensemble. ICML 2014: 1584-1592 - [c10]Jose H. Blanchet, Christopher Dolan, Henry Lam:
Robust rare-event performance analysis with natural non-convex constraints. WSC 2014: 595-603 - [c9]Aleksandrina Goeva, Henry Lam, Bo Zhang:
Reconstructing input models via simulation optimization. WSC 2014: 698-709 - [i3]Henry Lam, Zhenming Liu:
From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments. CoRR abs/1406.6084 (2014) - 2013
- [j2]Jijie Wang, Henry Lam:
Graph-based peak alignment algorithms for multiple liquid chromatography-mass spectrometry datasets. Bioinform. 29(19): 2469-2476 (2013) - [c8]Mung Chiang, Henry Lam, Zhenming Liu, H. Vincent Poor:
Why Steiner-tree type algorithms work for community detection. AISTATS 2013: 187-195 - [c7]Henry Lam, Soumyadip Ghosh:
Iterative methods for robust estimation under bivariate distributional uncertainty. WSC 2013: 193-204 - [i2]Christopher G. Brinton, Mung Chiang, Shaili Jain, Henry Lam, Zhenming Liu, Felix Ming Fai Wong:
Learning about social learning in MOOCs: From statistical analysis to generative model. CoRR abs/1312.2159 (2013) - 2012
- [c6]Henry Lam, Zhenming Liu, Michael Mitzenmacher, Xiaorui Sun, Yajun Wang:
Information dissemination via random walks in d-dimensional space. SODA 2012: 1612-1622 - [c5]Kai-Min Chung, Henry Lam, Zhenming Liu, Michael Mitzenmacher:
Chernoff-Hoeffding Bounds for Markov Chains: Generalized and Simplified. STACS 2012: 124-135 - [c4]Henry Lam:
Efficient importance sampling under partial information. WSC 2012: 41:1-41:12 - 2011
- [c3]Henry Lam:
Exact asymptotic for infinite-server queues. QTNA 2011: 101-106 - [c2]Jose H. Blanchet, Henry Lam:
Rare event simulation techniques. WSC 2011: 146-160 - [c1]Jose H. Blanchet, Henry Lam:
Importance sampling for actuarial cost analysis under a heavy traffic model. WSC 2011: 3817-3828 - [i1]Henry Lam, Zhenming Liu, Michael Mitzenmacher, Xiaorui Sun, Yajun Wang:
Information Dissemination via Random Walks in d-Dimensional Space. CoRR abs/1104.5268 (2011) - 2010
- [p1]Henry Lam, Ruedi Aebersold:
Spectral Library Searching for Peptide Identification via Tandem MS. Proteome Bioinformatics 2010: 95-103
2000 – 2009
- 2009
- [j1]Jose H. Blanchet, Peter W. Glynn, Henry Lam:
Rare event simulation for a slotted time M/G/s model. Queueing Syst. Theory Appl. 63(1-4): 33-57 (2009)
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-19 20:47 CET by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
