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On the benefits of output sparsity for multi-label classification., , , and . CoRR, (2017)Assembly micro-benchmark generator for characterizing Floating Point Units., , and . HPCS, page 407-415. IEEE, (2019)Interprétabilité et explicabilité de phénomènes prédits par de l'apprentissage machine., and . ROIA, 3 (3-4): 287-310 (2022)Fair regression via plug-in estimator and recalibration with statistical guarantees., , , , and . NeurIPS, (2020)Fair regression with Wasserstein barycenters., , , , and . NeurIPS, (2020)Numerical 'health check' for scientific codes: the CADNA approach., , , and . Comput. Phys. Commun., 176 (8): 507-521 (2007)Numerical Health Check of Industrial Simulation Codes from HPC Environments to New Hardware Technologies.. PPAM (2), volume 6068 of Lecture Notes in Computer Science, page 330-339. Springer, (2009)Confidence Sets for Classification., and . SLDS, volume 9047 of Lecture Notes in Computer Science, page 301-312. Springer, (2015)Algorithm 1029: Encapsulated Error, a Direct Approach to Evaluate Floating-Point Accuracy., , , and . ACM Trans. Math. Softw., 48 (4): 47:1-47:16 (December 2022)Active learning algorithm through the lens of rejection arguments., , , and . Mach. Learn., 113 (2): 753-788 (February 2024)