Deep neural networks exhibit remarkable performance, yet their black-box nature limits their utility in fields like healthcare where interpretability is crucial. Existing explainability approaches often sacrifice accuracy and lack quantifiable measures of prediction uncertainty. In this study, we introduce Conformal Prediction for Interpretable Neural Networks (CONFINE), a versatile framework that generates prediction sets with statistically robust uncertainty estimates instead of point predictions to enhance model transparency and reliability. CONFINE not only provides example-based explanations and confidence estimates for individual predictions but also boosts accuracy by up to 3.6%. We define a new metric, correct efficiency, to evaluate the fraction of prediction sets that contain precisely the correct label and show that CONFINE achieves correct efficiency of up to 3.3% higher than the original accuracy, matching or exceeding prior methods. CONFINE's marginal and class-conditional coverages attest to its validity across tasks spanning medical image classification to language understanding. Being adaptable to any pre-trained classifier, CONFINE marks a significant advance towards transparent and trustworthy deep learning applications in critical domains.
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence
Institute of Electronics, Information Engineering and Telecommunications - National Research Council of Italy (CNR-IEIIT)（电子信息与电信研究所 - 意大利国家科研理事会（CNR-IEIIT））；Politecnico di Torino - Department of Control and Computer Engineering (DAUIN)（托尼诺理工学院 - 控制与计算机工程系（DAUIN））；Rulex Innovation Labs（Rulex创新实验室）
Uncertainty-Aware Multimodal Learning via Conformal Shapley Intervals
Department of Statistics and Data Science, UCLA（统计与数据科学系， UCLA）；Merck & Co（默克公司）；Department of Biostatistics, UCLA（生物统计学系， UCLA）；Departments of Statistics and Data Science, and of Biostatistics, UCLA（统计与数据科学系以及生物统计学系， UCLA）
Improving the interpretability of GNN predictions through conformal-based graph sparsification
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；McGill University（麦吉尔大学）；Saarland University（萨尔大学）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
Conformal Prediction for Signal Temporal Logic Inference
Mechanical Engineering Department（机械工程系）；Department of Mechanical Engineering（机械工程系）；Lincoln Laboratory（林肯实验室）；Massachusetts Institute of Technology（麻省理工学院）
Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well
School of Computing and Information Technology（计算与信息科技学院）；Great Bay University（大湾大学）；Great Bay Institute for Advanced Study（大湾先进研究院）；Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems（广东省数学与神经动力系统重点实验室）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞智能与信息技术重点实验室）；Data and Artificial Intelligence Cluster, Department of Mathematics and Computer Science（数据与人工智能集群，数学与计算机科学系）；Eindhoven University of Technology（埃因霍温理工大学）
