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
Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma Grading
Biraja Ghoshal, Bhargab Ghoshal, Allan Tucker
When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints
Mahmoud Shoush, Marlon Dumas
Federated Learning with Uncertainty via Distilled Predictive Distributions
Shrey Bhatt, Aishwarya Gupta, Piyush Rai
Conformance Checking with Uncertainty via SMT (Extended Version)
Paolo Felli, Alessandro Gianola, Marco Montali, Andrey Rivkin, Sarah Winkler