b'Henry Lam'

research

∙ 06/09/2022

Evaluating Aleatoric Uncertainty via Conditional Generative Models

Aleatoric uncertainty quantification seeks for distributional knowledge ...

Ziyi Huang, et al. ∙

research

∙ 04/05/2022

Prediction Intervals for Simulation Metamodeling

Simulation metamodeling refers to the construction of lower-fidelity mod...

Henry Lam, et al. ∙

research

∙ 04/04/2022

Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling

Evaluating the performance of autonomous vehicles (AV) and their complex...

Mansur Arief, et al. ∙

research

∙ 01/31/2022

A Cheap Bootstrap Method for Fast Inference

The bootstrap is a versatile inference method that has proven powerful i...

Henry Lam, et al. ∙

research

∙ 01/31/2022

Generalized Bayesian Upper Confidence Bound with Approximate Inference for Bandit Problems

Bayesian bandit algorithms with approximate inference have been widely u...

Ziyi Huang, et al. ∙

research

∙ 12/03/2021

Efficient Calibration of Multi-Agent Market Simulators from Time Series with Bayesian Optimization

Multi-agent market simulation is commonly used to create an environment ...

Yuanlu Bai, et al. ∙

research

∙ 11/15/2021

Orthounimodal Distributionally Robust Optimization: Representation, Computation and Multivariate Extreme Event Applications

This paper studies a basic notion of distributional shape known as ortho...

Henry Lam, et al. ∙

research

∙ 11/12/2021

Higher-Order Coverage Errors of Batching Methods via Edgeworth Expansions on t-Statistics

While batching methods have been widely used in simulation and statistic...

Shengyi He, et al. ∙

research

∙ 11/03/2021

Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems

Rare-event simulation techniques, such as importance sampling (IS), cons...

Mansur Arief, et al. ∙

research

∙ 10/25/2021

Over-Conservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation

In rare-event simulation, an importance sampling (IS) estimator is regar...

Yuanlu Bai, et al. ∙

research

∙ 10/23/2021

Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence

Standard Monte Carlo computation is widely known to exhibit a canonical ...

Henry Lam, et al. ∙

research

∙ 10/23/2021

Quantifying Epistemic Uncertainty in Deep Learning

Uncertainty quantification is at the core of the reliability and robustn...

Ziyi Huang, et al. ∙

research

∙ 08/11/2021

Higher-Order Expansion and Bartlett Correctability of Distributionally Robust Optimization

Distributionally robust optimization (DRO) is a worst-case framework for...

Shengyi He, et al. ∙

research

∙ 06/21/2021

Complexity-Free Generalization via Distributionally Robust Optimization

Established approaches to obtain generalization bounds in data-driven op...

Henry Lam, et al. ∙

research

∙ 06/19/2021

Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling

The evaluation of rare but high-stakes events remains one of the main di...

Mengdi Xu, et al. ∙

research

∙ 05/27/2021

On the Impossibility of Statistically Improving Empirical Optimization: A Second-Order Stochastic Dominance Perspective

When the underlying probability distribution in a stochastic optimizatio...

Henry Lam, et al. ∙

research

∙ 05/27/2021

Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set

Stochastic simulation aims to compute output performance for complex mod...

Yuanlu Bai, et al. ∙

research

∙ 05/19/2021

Distributionally Constrained Black-Box Stochastic Gradient Estimation and Optimization

We consider stochastic gradient estimation using only black-box function...

Henry Lam, et al. ∙

research

∙ 02/26/2021

Learning Prediction Intervals for Regression: Generalization and Calibration

We study the generation of prediction intervals in regression for uncert...

Haoxian Chen, et al. ∙

research

∙ 02/21/2021

Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation

In solving simulation-based stochastic root-finding or optimization prob...

Shengyi He, et al. ∙

research

∙ 02/03/2021

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

We study a methodology to tackle the NASA Langley Uncertainty Quantifica...

Yuanlu Bai, et al. ∙

research

∙ 12/10/2020

Efficient Learning for Clustering and Optimizing Context-Dependent Designs

We consider a simulation optimization problem for a context-dependent de...

Haidong Li, et al. ∙

research

∙ 12/10/2020

Context-dependent Ranking and Selection under a Bayesian Framework

We consider a context-dependent ranking and selection problem. The best ...

Haidong Li, et al. ∙

research

∙ 10/10/2020

Rare-Event Simulation for Neural Network and Random Forest Predictors

We study rare-event simulation for a class of problems where the target ...

Yuanlu Bai, et al. ∙

research

∙ 07/08/2020

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

We consider stochastic gradient estimation using noisy black-box functio...

Henry Lam, et al. ∙

research

∙ 06/28/2020

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

Evaluating the reliability of intelligent physical systems against rare ...

Mansur Arief, et al. ∙

research

∙ 06/28/2020

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

We study a methodology to tackle the NASA Langley Uncertainty Quantifica...

Yuanlu Bai, et al. ∙

research

∙ 10/14/2019

Robust Importance Weighting for Covariate Shift

In many learning problems, the training and testing data follow differen...

Henry Lam, et al. ∙

research

∙ 10/12/2019

Efficient Inference and Exploration for Reinforcement Learning

Despite an ever growing literature on reinforcement learning algorithms ...

Yi Zhu, et al. ∙

research

∙ 04/25/2019

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

We consider optimization problems with uncertain constraints that need t...

Henry Lam, et al. ∙

research

∙ 04/19/2019

Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation

Safety evaluation of autonomous vehicles is extensively studied recently...

Zhiyuan Huang, et al. ∙

research

∙ 02/12/2019

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

Biased stochastic estimators, such as finite-differences for noisy gradi...

Henry Lam, et al. ∙

research

∙ 11/11/2018

Subsampling to Enhance Efficiency in Input Uncertainty Quantification

In stochastic simulation, input uncertainty refers to the output variabi...

Henry Lam, et al. ∙

research

∙ 10/05/2018

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...

Henry Lam, et al. ∙

research

∙ 09/09/2018

Synthesis of Different Autonomous Vehicles Test Approaches

Currently, the most prevalent way to evaluate an autonomous vehicle is t...

Zhiyuan Huang, et al. ∙

research

∙ 11/02/2017

On Optimization over Tail Distributions

We investigate the use of optimization to compute bounds for extremal pe...

Clementine Mottet, et al. ∙

research

∙ 10/01/2017

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

Evaluation and validation of complicated control systems are crucial to ...

Zhiyuan Huang, et al. ∙

research

∙ 10/19/2016

Robust and Parallel Bayesian Model Selection

Effective and accurate model selection is an important problem in modern...

Michael Minyi Zhang, et al. ∙

research

∙ 07/10/2016

Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers

The safety of Automated Vehicles (AVs) must be assured before their rele...

Ding Zhao, et al. ∙

Henry Lam

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