Henry Lam

According to our database1, Henry Lam authored at least 89 papers between 2009 and 2021.

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2021
Minimax efficient finite-difference stochastic gradient estimators using black-box function evaluations.
Oper. Res. Lett., 2021

Learning-Based Robust Optimization: Procedures and Statistical Guarantees.
Manag. Sci., 2021

Efficient Calibration of Multi-Agent Market Simulators from Time Series with Bayesian Optimization.
CoRR, 2021

Quantifying Epistemic Uncertainty in Deep Learning.
CoRR, 2021

Complexity-Free Generalization via Distributionally Robust Optimization.
CoRR, 2021

Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling.
CoRR, 2021

Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set.
CoRR, 2021

Learning Prediction Intervals for Regression: Generalization and Calibration.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Parametric Scenario Optimization under Limited Data: A Distributionally Robust Optimization View.
ACM Trans. Model. Comput. Simul., 2020

Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling.
Oper. Res., 2020

Rare-Event Simulation for Neural Network and Random Forest Predictors.
CoRR, 2020

Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy.
CoRR, 2020

Sample Average Approximation For Functional Decisions Under Shape Constraints.
Proceedings of the Winter Simulation Conference, 2020

Context-Dependent Ranking and Selection under a Bayesian Framework.
Proceedings of the Winter Simulation Conference, 2020

Optimally Tuning Finite-Difference Estimators.
Proceedings of the Winter Simulation Conference, 2020

Distributionally Constrained Stochastic Gradient Estimation Using Noisy Function Evaluations.
Proceedings of the Winter Simulation Conference, 2020

Calibrating Input Parameters via Eligibility Sets.
Proceedings of the Winter Simulation Conference, 2020

On the Error of Naive Rare-Event Monte Carlo Estimator.
Proceedings of the Winter Simulation Conference, 2020

Robust Importance Weighting for Covariate Shift.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Constrained Reinforcement Learning via Policy Splitting.
Proceedings of The 12th Asian Conference on Machine Learning, 2020

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

Optimization-Based Calibration of Simulation Input Models.
Oper. Res., 2019

Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees.
Oper. Res., 2019

Efficient Inference and Exploration for Reinforcement Learning.
CoRR, 2019

Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation.
CoRR, 2019

From Data to Stochastic Modeling and Decision Making: What Can We Do Better?
Asia Pac. J. Oper. Res., 2019

Minimax Efficient Finite-Difference Gradient Estimators.
Proceedings of the 2019 Winter Simulation Conference, 2019

On The Stability of Kernelized Control Functionals On Partial And Biased Stochastic Inputs.
Proceedings of the 2019 Winter Simulation Conference, 2019

Validating Optimization with Uncertain Constraints.
Proceedings of the 2019 Winter Simulation Conference, 2019

Random Perturbation and Bagging to Quantify Input Uncertainty.
Proceedings of the 2019 Winter Simulation Conference, 2019

On The Impacts of Tail Model Uncertainty in Rare-Event Estimation.
Proceedings of the 2019 Winter Simulation Conference, 2019

Evaluation Uncertainty in Data-Driven Self-Driving Testing.
Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, 2019

2018
Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers.
IEEE Trans. Intell. Transp. Syst., 2018

Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models.
IEEE Trans. Intell. Transp. Syst., 2018

Sensitivity to Serial Dependency of Input Processes: A Robust Approach.
Manag. Sci., 2018

Robust and parallel Bayesian model selection.
Comput. Stat. Data Anal., 2018

Assessing solution Quality in stochastic Optimization via bootstrap Aggregating.
Proceedings of the 2018 Winter Simulation Conference, 2018

Subsampling variance for input uncertainty Quantification.
Proceedings of the 2018 Winter Simulation Conference, 2018

Sampling uncertain Constraints under parametric distributions.
Proceedings of the 2018 Winter Simulation Conference, 2018

On efficiencies of stochastic Optimization Procedures under Importance Sampling.
Proceedings of the 2018 Winter Simulation Conference, 2018

Rare-Event simulation without Structural Information: a Learning-based Approach.
Proceedings of the 2018 Winter Simulation Conference, 2018

Designing Importance samplers to simulate Machine Learning Predictors via Optimization.
Proceedings of the 2018 Winter Simulation Conference, 2018

Constructing simulation output Intervals under input uncertainty via Data sectioning.
Proceedings of the 2018 Winter Simulation Conference, 2018

Achieving Optimal Bias-variance Tradeoff in Online derivative estimation.
Proceedings of the 2018 Winter Simulation Conference, 2018

Revisiting Direct bootstrap resampling for input Model uncertainty.
Proceedings of the 2018 Winter Simulation Conference, 2018

Sequential Learning under Probabilistic Constraints.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Synthesis of Different Autonomous Vehicles Test Approaches.
Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods.
Proceedings of the 2018 Annual American Control Conference, 2018

2017
Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques.
IEEE Trans. Intell. Transp. Syst., 2017

The empirical likelihood approach to quantifying uncertainty in sample average approximation.
Oper. Res. Lett., 2017

Tail Analysis Without Parametric Models: A Worst-Case Perspective.
Oper. Res., 2017

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods.
CoRR, 2017

Uncertainty quantification on simulation analysis driven by random forests.
Proceedings of the 2017 Winter Simulation Conference, 2017

Improving prediction from stochastic simulation via model discrepancy learning.
Proceedings of the 2017 Winter Simulation Conference, 2017

Sequential experimentation to efficiently test automated vehicles.
Proceedings of the 2017 Winter Simulation Conference, 2017

Computing worst-case expectations given marginals via simulation.
Proceedings of the 2017 Winter Simulation Conference, 2017

An accelerated testing approach for automated vehicles with background traffic described by joint distributions.
Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems, 2017

Towards affordable on-track testing for autonomous vehicle - A Kriging-based statistical approach.
Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems, 2017

Evaluation of automated vehicles in the frontal cut-in scenario - An enhanced approach using piecewise mixture models.
Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

2016
Robust Sensitivity Analysis for Stochastic Systems.
Math. Oper. Res., 2016

Accelerated Evaluation of Automated Vehicles based on Importance Sampling Techniques.
CoRR, 2016

Accelerated Evaluation of Automated Vehicles using Piecewise Mixture Distribution Models.
CoRR, 2016

Learning stochastic model discrepancy.
Proceedings of the Winter Simulation Conference, 2016

The empirical likelihood approach to simulation input uncertainty.
Proceedings of the Winter Simulation Conference, 2016

Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation.
Proceedings of the Winter Simulation Conference, 2016

Approximating data-driven joint chance-constrained programs via uncertainty set construction.
Proceedings of the Winter Simulation Conference, 2016

2015
A Bayesian Approach for Online Classifier Ensemble.
CoRR, 2015

Quantifying uncertainty in sample average approximation.
Proceedings of the 2015 Winter Simulation Conference, 2015

Simulating tail events with unspecified tail models.
Proceedings of the 2015 Winter Simulation Conference, 2015

A statistical perspective on linear programs with uncertain parameters.
Proceedings of the 2015 Winter Simulation Conference, 2015

Mirror descent stochastic approximation for computing worst-case stochastic input models.
Proceedings of the 2015 Winter Simulation Conference, 2015

2014
Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model.
IEEE Trans. Learn. Technol., 2014

Rare-Event Simulation for Many-Server Queues.
Math. Oper. Res., 2014

From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments.
CoRR, 2014

Reconstructing input models via simulation optimization.
Proceedings of the 2014 Winter Simulation Conference, 2014

Robust rare-event performance analysis with natural non-convex constraints.
Proceedings of the 2014 Winter Simulation Conference, 2014

A Bayesian Framework for Online Classifier Ensemble.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Graph-based peak alignment algorithms for multiple liquid chromatography-mass spectrometry datasets.
Bioinform., 2013

Iterative methods for robust estimation under bivariate distributional uncertainty.
Proceedings of the Winter Simulations Conference: Simulation Making Decisions in a Complex World, 2013

Why Steiner-tree type algorithms work for community detection.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Efficient importance sampling under partial information.
Proceedings of the Winter Simulation Conference, 2012

Chernoff-Hoeffding Bounds for Markov Chains: Generalized and Simplified.
Proceedings of the 29th International Symposium on Theoretical Aspects of Computer Science, 2012

Information dissemination via random walks in <i>d</i>-dimensional space.
Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, 2012

2011
Information Dissemination via Random Walks in d-Dimensional Space
CoRR, 2011

Importance sampling for actuarial cost analysis under a heavy traffic model.
Proceedings of the Winter Simulation Conference 2011, 2011

Rare event simulation techniques.
Proceedings of the Winter Simulation Conference 2011, 2011

2010
Spectral Library Searching for Peptide Identification via Tandem MS.
Proceedings of the Proteome Bioinformatics, 2010

2009
Rare event simulation for a slotted time <i>M</i>/<i>G</i>/<i>s</i> model.
Queueing Syst. Theory Appl., 2009


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