457 citations · 696 across the 72 of their papers we have counts for
9 papers · 1 filter
Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework
Garud Iyengar, Henry Lam, Tianyu Wang
Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approaches for solving stochastic optimization problems that appear in operations manag…
Adaptive Data Fusion for Multi-task Non-smooth Optimization
Henry Lam, Kaizheng Wang, Yuhang Wu +1
We study the problem of multi-task non-smooth optimization that arises ubiquitously in statistical learning, decision-making and risk management. We develop a data fusion approach…
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mengdi Xu, Peide Huang, Yaru Niu +8
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators. Robust RL has been applied to deal with task ambiguity, but may result i…
Bootstrap in High Dimension with Low Computation
Henry Lam, Zhenyuan Liu
The bootstrap is a popular data-driven method to quantify statistical uncertainty, but for modern high-dimensional problems, it could suffer from huge computational costs due to th…
Evaluating Aleatoric Uncertainty via Conditional Generative Models
Ziyi Huang, Henry Lam, Haofeng Zhang
Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learn…
Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling
Mansur Arief, Zhepeng Cen, Zhenyuan Liu +4
Evaluating the performance of autonomous vehicles (AV) and their complex subsystems to high precision under naturalistic circumstances remains a challenge, especially when failure…