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
Prioritizing emergency evacuations under compounding levels of uncertainty
Lisa J. Einstein, Robert J. Moss, Mykel J. Kochenderfer
Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty
Stephen Brown, William L. Rodi, Marco Seracini, Chen Gu, Michael Fehler, James Faulds, Connor M. Smith, Sven Treitel
A Multiple Criteria Decision Analysis based Approach to Remove Uncertainty in SMP Models
Gokul Yenduri, Thippa Reddy Gadekallu
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts
Neeraj Wagh, Jionghao Wei, Samarth Rawal, Brent M. Berry, Yogatheesan Varatharajah
Characterizing Uncertainty in the Visual Text Analysis Pipeline
Pantea Haghighatkhah, Mennatallah El-Assady, Jean-Daniel Fekete, Narges Mahyar, Carita Paradis, Vasiliki Simaki, Bettina Speckmann