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Henry Lam
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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
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
Uncertainty Quantification and Exploration for Reinforcement Learning. Oper. Res. 72(4): 1689-1709 (2024) - [j27]Yuanlu Bai, Zhiyuan Huang
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 - [i40]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) - [i39]Huajie Qian, Donghao Ying, Henry Lam, Wotao Yin:
Bagging Improves Generalization Exponentially. CoRR abs/2405.14741 (2024) - [i38]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) - [i37]Ziyi Huang, Henry Lam, Haofeng Zhang:
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits. CoRR abs/2406.14071 (2024) - 2023
- [j26]Henry Lam
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
A New Likelihood Ratio Method for Training Artificial Neural Networks. INFORMS J. Comput. 34(1): 638-655 (2022) - [j23]Henry Lam
Subsampling to Enhance Efficiency in Input Uncertainty Quantification. Oper. Res. 70(3): 1891-1913 (2022) - [j22]Henry Lam
General Feasibility Bounds for Sample Average Approximation via Vapnik-Chervonenkis Dimension. SIAM J. Optim. 32(2): 1471-1497 (2022) - [j21]Yuanlu Bai, Zhiyuan Huang
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
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
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
Learning-Based Robust Optimization: Procedures and Statistical Guarantees. Manag. Sci. 67(6): 3447-3467 (2021) - [j19]Henry Lam
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
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
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
Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling. Oper. Res. 68(6): 1896-1912 (2020) - [j17]Henry Lam
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
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
Optimization-Based Calibration of Simulation Input Models. Oper. Res. 67(5): 1362-1382 (2019) - [c44]Zhiyuan Huang, Mansur Arief
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
Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers. IEEE Trans. Intell. Transp. Syst. 19(3): 733-744 (2018) - [j9]Zhiyuan Huang
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
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods. ACC 2018: 4796-4802 - [c37]Zhiyuan Huang, Mansur Arief
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
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
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
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
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
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
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
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
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
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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