b'Henry Lam'

Learning Prediction Intervals for Regression: Generalization and Calibration

We study the generation of prediction intervals in regression for uncert...
02/26/2021 ∙ by Haoxian Chen, et al. ∙ 0 ∙ share

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Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation

In solving simulation-based stochastic root-finding or optimization prob...
02/21/2021 ∙ by Shengyi He, et al. ∙ 0 ∙ share

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Model Calibration via Distributionally Robust Optimization: On the NASA Langley Uncertainty Quantification Challenge

We study a methodology to tackle the NASA Langley Uncertainty Quantifica...
02/03/2021 ∙ by Yuanlu Bai, et al. ∙ 0 ∙ share

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Efficient Learning for Clustering and Optimizing Context-Dependent Designs

We consider a simulation optimization problem for a context-dependent de...
12/10/2020 ∙ by Haidong Li, et al. ∙ 0 ∙ share

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Context-dependent Ranking and Selection under a Bayesian Framework

We consider a context-dependent ranking and selection problem. The best ...
12/10/2020 ∙ by Haidong Li, et al. ∙ 0 ∙ share

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Rare-Event Simulation for Neural Network and Random Forest Predictors

We study rare-event simulation for a class of problems where the target ...
10/10/2020 ∙ by Yuanlu Bai, et al. ∙ 0 ∙ share

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Minimax Efficient Finite-Difference Stochastic Gradient Estimators Using Black-Box Function Evaluations

We consider stochastic gradient estimation using noisy black-box functio...
07/08/2020 ∙ by Henry Lam, et al. ∙ 0 ∙ share

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

Evaluating the reliability of intelligent physical systems against rare ...
06/28/2020 ∙ by Mansur Arief, et al. ∙ 0 ∙ share

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A Distributionally Robust Optimization Approach to the NASA Langley Uncertainty Quantification Challenge

We study a methodology to tackle the NASA Langley Uncertainty Quantifica...
06/28/2020 ∙ by Yuanlu Bai, et al. ∙ 0 ∙ share

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Robust Importance Weighting for Covariate Shift

In many learning problems, the training and testing data follow differen...
10/14/2019 ∙ by Henry Lam, et al. ∙ 0 ∙ share

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Efficient Inference and Exploration for Reinforcement Learning

Despite an ever growing literature on reinforcement learning algorithms ...
10/12/2019 ∙ by Yi Zhu, et al. ∙ 47 ∙ share

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Parametric Scenario Optimization under Limited Data: A Distributionally Robust Optimization View

We consider optimization problems with uncertain constraints that need t...
04/25/2019 ∙ by Henry Lam, et al. ∙ 0 ∙ share

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Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation

Safety evaluation of autonomous vehicles is extensively studied recently...
04/19/2019 ∙ by Zhiyuan Huang, et al. ∙ 0 ∙ share

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Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective

Biased stochastic estimators, such as finite-differences for noisy gradi...
02/12/2019 ∙ by Henry Lam, et al. ∙ 0 ∙ share

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Subsampling to Enhance Efficiency in Input Uncertainty Quantification

In stochastic simulation, input uncertainty refers to the output variabi...
11/11/2018 ∙ by Henry Lam, et al. ∙ 0 ∙ share

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Bounding Optimality Gap in Stochastic Optimization via Bagging: Statistical Efficiency and Stability

We study a statistical method to estimate the optimal value, and the opt...
10/05/2018 ∙ by Henry Lam, et al. ∙ 0 ∙ share

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Synthesis of Different Autonomous Vehicles Test Approaches

Currently, the most prevalent way to evaluate an autonomous vehicle is t...
09/09/2018 ∙ by Zhiyuan Huang, et al. ∙ 0 ∙ share

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On Optimization over Tail Distributions

We investigate the use of optimization to compute bounds for extremal pe...
11/02/2017 ∙ by Clementine Mottet, et al. ∙ 0 ∙ share

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A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods

Evaluation and validation of complicated control systems are crucial to ...
10/01/2017 ∙ by Zhiyuan Huang, et al. ∙ 0 ∙ share

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Robust and Parallel Bayesian Model Selection

Effective and accurate model selection is an important problem in modern...
10/19/2016 ∙ by Michael Minyi Zhang, et al. ∙ 0 ∙ share

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Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers

The safety of Automated Vehicles (AVs) must be assured before their rele...
07/10/2016 ∙ by Ding Zhao, et al. ∙ 0 ∙ share

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Henry Lam

Featured Co-authors

Yi Zhu

Ding Zhao

Jing Dong

Lizhen Lin

Xinyu Zhang

Huei Peng

Wenhao Ding

Michael Minyi Zhang

Mansur Arief

Zhiyuan Huang

Yijie Peng

Learning Prediction Intervals for Regression: Generalization and Calibration

Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation

Model Calibration via Distributionally Robust Optimization: On the NASA Langley Uncertainty Quantification Challenge

Efficient Learning for Clustering and Optimizing Context-Dependent Designs

Context-dependent Ranking and Selection under a Bayesian Framework

Rare-Event Simulation for Neural Network and Random Forest Predictors

Minimax Efficient Finite-Difference Stochastic Gradient Estimators Using Black-Box Function Evaluations

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

A Distributionally Robust Optimization Approach to the NASA Langley Uncertainty Quantification Challenge

Robust Importance Weighting for Covariate Shift

Efficient Inference and Exploration for Reinforcement Learning

Parametric Scenario Optimization under Limited Data: A Distributionally Robust Optimization View

Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation

Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective

Subsampling to Enhance Efficiency in Input Uncertainty Quantification

Bounding Optimality Gap in Stochastic Optimization via Bagging: Statistical Efficiency and Stability

Synthesis of Different Autonomous Vehicles Test Approaches

On Optimization over Tail Distributions

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods

Robust and Parallel Bayesian Model Selection

Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers