Pattern Recognition Journal Special Issue on Conformal Prediction and Distribution-Free Uncertainty Quantification

Call for Papers: Pattern Recognition Journal Special Issue on Conformal Prediction and Distribution-Free Uncertainty Quantification

The Pattern Recognition Journal is inviting submissions for a special issue on Conformal Prediction (CP) and Distribution-Free Uncertainty Quantification. The deadline for submissions is September 30, 2024, and final notifications will be sent out by May 31, 2025. The review process will begin upon each submission, and notifications will be communicated accordingly.

CP is a versatile framework that offers prediction regions with valid coverage guarantees under minimal assumptions, making it an important tool for complex Pattern Recognition tasks in various fields. Recent advancements in CP, such as Conformal Predictive Distributions, Conformal change-point detection, Venn-Abers predictors, and jackknife+, have expanded its applicability and effectiveness.

Topics of interest for this special issue include theoretical analyses and performance guarantees of CP, novel CP approaches and conformity measures, conformal predictive distributions, conformal change-point and anomaly detection, Venn-Abers and other multiprobability prediction approaches, post-hoc calibration through CP, decision-making through CP and distribution-free uncertainty quantification, implementations of CP frameworks and algorithms, CP for explainable machine learning and Fairness, Accountability and Transparency (FAT), and CP applications in various fields.

Guest editors for this special issue are Harris Papadopoulos of Frederick University, Khuong An Nguyen of Royal Holloway University of London, and Vineeth Balasubramanian of Indian Institute of Technology Hyderabad.

For more information and to submit your manuscript, please visit this link and select the article type “VSI: Conformal Prediction”.