Uncertainty Quantification
Uncertainty quantification (UQ) aims to assess and represent the confidence in predictions made by machine learning models, crucial for high-stakes applications where reliable predictions are paramount. Current research focuses on developing robust UQ methods, particularly addressing biases in predictions and efficiently quantifying uncertainty in large language models and deep neural networks, often employing techniques like conformal prediction, Bayesian methods, and ensemble learning. The ability to reliably quantify uncertainty enhances the trustworthiness and applicability of machine learning across diverse fields, from healthcare diagnostics and autonomous driving to climate modeling and drug discovery.
Papers
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Yuyang Wang
CLOSURE: Fast Quantification of Pose Uncertainty Sets
Yihuai Gao, Yukai Tang, Han Qi, Heng Yang
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, Kamalika Das
From Displacements to Distributions: A Machine-Learning Enabled Framework for Quantifying Uncertainties in Parameters of Computational Models
Taylor Roper, Harri Hakula, Troy Butler
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming
Yongquan Qu, Mohamed Aziz Bhouri, Pierre Gentine
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
Luis Itza Vazquez-Salazar, Silvan Käser, Markus Meuwly
Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification
Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
Himanshu Sharma, Lukáš Novák, Michael D. Shields