High Uncertainty Anticipation
High uncertainty anticipation focuses on developing methods to accurately quantify and manage uncertainty in model predictions across diverse fields, aiming to improve the reliability and trustworthiness of AI systems. Current research emphasizes integrating uncertainty estimation into various model architectures, including neural networks, diffusion models, and graph neural networks, often employing techniques like Bayesian methods, conformal prediction, and ensemble methods. This work is crucial for deploying AI in high-stakes applications like healthcare, autonomous driving, and finance, where reliable uncertainty quantification is paramount for safe and effective decision-making.
Papers
A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty
Michael Barnett, William Brock, Lars Peter Hansen, Ruimeng Hu, Joseph Huang
Knowledge from Uncertainty in Evidential Deep Learning
Cai Davies, Marc Roig Vilamala, Alun D. Preece, Federico Cerutti, Lance M. Kaplan, Supriyo Chakraborty
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach
Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling
Ke Yu, Stephen Albro, Giulia DeSalvo, Suraj Kothawade, Abdullah Rashwan, Sasan Tavakkol, Kayhan Batmanghelich, Xiaoqi Yin
To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data
Hechuan Wen, Tong Chen, Li Kheng Chai, Shazia Sadiq, Kai Zheng, Hongzhi Yin
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty
Wei Jiang, Zhongkai Yi, Li Wang, Hanwei Zhang, Jihai Zhang, Fangquan Lin, Cheng Yang