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Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.

, , , , , , и . CVPR, стр. 10400-10411. IEEE, (2022)

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Matthäus Janczyk

Synthese funktionalisierter Trinukleotide und ihre Anwendung bei der Randomisierung. Uni Greifswald, (2009)

Matthäus Rieder

Matthäus Haugg

Hans Matthäus

Matthäus Stubenvoll

 

Другие публикации лиц с тем же именем

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations., , , , и . CoRR, (2022)Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers., , , , , , и . CVPR, стр. 10400-10411. IEEE, (2022)Individual Preference Stability for Clustering., , , , , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 197-246. PMLR, (2022)Equalized odds postprocessing under imperfect group information., , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 1770-1780. PMLR, (2020)Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information., , и . CoRR, (2019)Adaptive Sampling to Reduce Disparate Performance., , , , и . CoRR, (2020)Evaluating the Fairness of Discriminative Foundation Models in Computer Vision., , , , , и . AIES, стр. 809-833. ACM, (2023)Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information., , , , и . FAccT, стр. 206-214. ACM, (2021)Measuring Fairness of Rankings under Noisy Sensitive Information., , , , и . FAccT, стр. 2263-2279. ACM, (2022)Guarantees for Spectral Clustering with Fairness Constraints., , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 3458-3467. PMLR, (2019)