Stein Variational Inference for Uncertainty Quantification: Breaking the Computational, Data, and Human Bottlenecks
Abstract
Uncertain quantification (UQ) is a central component of countless computational and real-world applications. However, with the unprecedented surge of scale and complexity of modern applications, UQ in modern systems is largely hindered by three major bottlenecks:1) the computational bottleneck for developing fast and scalable algorithms for reasoning with computationally intensive systems; 2) the data bottleneck due to the high cost of collecting informative measurement in critical areas; and 3) the human bottleneck due to the challenge of conveying the results of increasingly sophisticated algorithms to human decision makers in a reliable and transparent way. This project will tackle the key challenges in the computational, data and human bottlenecks by developing and enriching a fundamentally new paradigm for uncertainty quantification and approximate inference called Stein variational inference. This new framework will yield a spectrum of novel algorithms with both practical efficiency and attractive theoretical properties, offering a powerful approach for tackling the key challenges in UQ, including fast computation, optimal data acquisition, and human interpretability.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 05, 2021
- Source ID
- N000142112665
Entities
People
- Qiang Liu
Organizations
- Office of Naval Research
- United States Navy
- University of Texas at Austin