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06/24/2023
Smoothed f-Divergence Distributionally Robust Optimization: Exponential Rate Efficiency and Complexity-Free Calibration
In data-driven optimization, sample average approximation is known to su...
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06/09/2023
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
Uncertainty quantification (UQ) is important for reliability assessment ...
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05/28/2023
Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes
Many event sequence data exhibit mutually exciting or inhibiting pattern...
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05/03/2023
Uncertainty Quantification and Confidence Intervals for Naive Rare-Event Estimators
We consider the estimation of rare-event probabilities using sample prop...
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04/13/2023
Estimate-Then-Optimize Versus Integrated-Estimation-Optimization: A Stochastic Dominance Perspective
In data-driven stochastic optimization, model parameters of the underlyi...
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01/03/2023
A Distributionally Robust Optimization Framework for Extreme Event Estimation
Conventional methods for extreme event estimation rely on well-chosen pa...
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12/03/2022
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation
Empirical risk minimization (ERM) and distributionally robust optimizati...
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10/22/2022
Adaptive Data Fusion for Multi-task Non-smooth Optimization
We study the problem of multi-task non-smooth optimization that arises u...
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10/21/2022
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
One key challenge for multi-task Reinforcement learning (RL) in practice...
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10/20/2022
Finite-Sample Coverage Errors of the Cheap Bootstrap With Minimal Resampling Effort
The bootstrap is a popular data-driven method to quantify statistical un...
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06/09/2022
Evaluating Aleatoric Uncertainty via Conditional Generative Models
Aleatoric uncertainty quantification seeks for distributional knowledge ...
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04/05/2022
Prediction Intervals for Simulation Metamodeling
Simulation metamodeling refers to the construction of lower-fidelity mod...
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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...
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01/31/2022
A Cheap Bootstrap Method for Fast Inference
The bootstrap is a versatile inference method that has proven powerful i...
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01/31/2022
Generalized Bayesian Upper Confidence Bound with Approximate Inference for Bandit Problems
Bayesian bandit algorithms with approximate inference have been widely u...
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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 ...
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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...
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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...
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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...
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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...
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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 ...
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10/23/2021
Quantifying Epistemic Uncertainty in Deep Learning
Uncertainty quantification is at the core of the reliability and robustn...
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08/11/2021
Higher-Order Expansion and Bartlett Correctability of Distributionally Robust Optimization
Distributionally robust optimization (DRO) is a worst-case framework for...
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06/21/2021
Complexity-Free Generalization via Distributionally Robust Optimization
Established approaches to obtain generalization bounds in data-driven op...
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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...
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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...
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05/27/2021
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Stochastic simulation aims to compute output performance for complex mod...
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05/19/2021
Distributionally Constrained Black-Box Stochastic Gradient Estimation and Optimization
We consider stochastic gradient estimation using only black-box function...
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02/26/2021
Learning Prediction Intervals for Regression: Generalization and Calibration
We study the generation of prediction intervals in regression for uncert...
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02/21/2021
Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation
In solving simulation-based stochastic root-finding or optimization prob...
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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...
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12/10/2020
Efficient Learning for Clustering and Optimizing Context-Dependent Designs
We consider a simulation optimization problem for a context-dependent de...
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12/10/2020
Context-dependent Ranking and Selection under a Bayesian Framework
We consider a context-dependent ranking and selection problem. The best ...
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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 ...
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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...
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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 ...
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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...
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10/14/2019
Robust Importance Weighting for Covariate Shift
In many learning problems, the training and testing data follow differen...
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10/12/2019
Efficient Inference and Exploration for Reinforcement Learning
Despite an ever growing literature on reinforcement learning algorithms ...
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04/25/2019
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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04/19/2019
Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation
Safety evaluation of autonomous vehicles is extensively studied recently...
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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...
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11/11/2018
Subsampling to Enhance Efficiency in Input Uncertainty Quantification
In stochastic simulation, input uncertainty refers to the output variabi...
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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...
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09/09/2018
Synthesis of Different Autonomous Vehicles Test Approaches
Currently, the most prevalent way to evaluate an autonomous vehicle is t...
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11/02/2017
On Optimization over Tail Distributions
We investigate the use of optimization to compute bounds for extremal pe...
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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 ...
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10/19/2016
Robust and Parallel Bayesian Model Selection
Effective and accurate model selection is an important problem in modern...
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07/10/2016
