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Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure.

, , and . NeurIPS, (2022)

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PUNCC: a Python Library for Predictive Uncertainty Calibration and Conformalization., , and . COPA, volume 204 of Proceedings of Machine Learning Research, page 582-601. PMLR, (2023)How to design a dataset compliant with an ML-based system ODD?, , , , , , , , , and 1 other author(s). CoRR, (2024)Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure., , and . NeurIPS, (2022)Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis., , , , , , and . CVPR, page 16153-16163. IEEE, (2023)Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning., , and . SEC, page 436-443. IEEE, (2022)Object Detection with Probabilistic Guarantees: A Conformal Prediction Approach., , , , , and . SAFECOMP Workshops, volume 13415 of Lecture Notes in Computer Science, page 316-329. Springer, (2022)FUNN: Flexible Unsupervised Neural Network., and . CoRR, (2018)How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks., , , and . WACV, page 1565-1575. IEEE, (2022)Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis., , , , , and . NeurIPS, page 26005-26014. (2021)Xplique: A Deep Learning Explainability Toolbox., , , , , , , , , and 5 other author(s). CoRR, (2022)