
Learning Prediction Intervals for Regression: Generalization and Calibration
We study the generation of prediction intervals in regression for uncert...
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Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation
In solving simulationbased stochastic rootfinding or optimization prob...
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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...
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Efficient Learning for Clustering and Optimizing ContextDependent Designs
We consider a simulation optimization problem for a contextdependent de...
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Contextdependent Ranking and Selection under a Bayesian Framework
We consider a contextdependent ranking and selection problem. The best ...
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RareEvent Simulation for Neural Network and Random Forest Predictors
We study rareevent simulation for a class of problems where the target ...
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Minimax Efficient FiniteDifference Stochastic Gradient Estimators Using BlackBox Function Evaluations
We consider stochastic gradient estimation using noisy blackbox functio...
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Deep Probabilistic Accelerated Evaluation: A Certifiable RareEvent Simulation Methodology for BlackBox Autonomy
Evaluating the reliability of intelligent physical systems against rare ...
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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...
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Robust Importance Weighting for Covariate Shift
In many learning problems, the training and testing data follow differen...
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Efficient Inference and Exploration for Reinforcement Learning
Despite an ever growing literature on reinforcement learning algorithms ...
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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...
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Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation
Safety evaluation of autonomous vehicles is extensively studied recently...
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Enhanced Balancing of BiasVariance Tradeoff in Stochastic Estimation: A Minimax Perspective
Biased stochastic estimators, such as finitedifferences for noisy gradi...
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Subsampling to Enhance Efficiency in Input Uncertainty Quantification
In stochastic simulation, input uncertainty refers to the output variabi...
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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...
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Synthesis of Different Autonomous Vehicles Test Approaches
Currently, the most prevalent way to evaluate an autonomous vehicle is t...
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On Optimization over Tail Distributions
We investigate the use of optimization to compute bounds for extremal pe...
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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 ...
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Robust and Parallel Bayesian Model Selection
Effective and accurate model selection is an important problem in modern...
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Accelerated Evaluation of Automated Vehicles in CarFollowing Maneuvers
The safety of Automated Vehicles (AVs) must be assured before their rele...
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Henry Lam
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