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Guarantees for Spectral Clustering with Fairness Constraints.

M. Kleindessner, S. Samadi, P. Awasthi, and J. Morgenstern. ICML, volume 97 of Proceedings of Machine Learning Research, page 3458-3467. PMLR, (2019)

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Matthäus Janczyk

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

Matthäus Pietz

Matthäus Bartsch

Gabor Matthäus

Horst Matthäus

Other publications of authors with the same name

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations.A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox. CoRR, (2022)Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B. Schölkopf, and C. Russell. CVPR, page 10400-10411. IEEE, (2022)Individual Preference Stability for Clustering.S. Ahmadi, P. Awasthi, S. Khuller, M. Kleindessner, J. Morgenstern, P. Sukprasert, and A. Vakilian. ICML, volume 162 of Proceedings of Machine Learning Research, page 197-246. PMLR, (2022)Backward-Compatible Prediction Updates: A Probabilistic Approach.F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler. NeurIPS, page 116-128. (2021)Guarantees for Spectral Clustering with Fairness Constraints.M. Kleindessner, S. Samadi, P. Awasthi, and J. Morgenstern. ICML, volume 97 of Proceedings of Machine Learning Research, page 3458-3467. PMLR, (2019)Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis.M. Kleindessner, and U. von Luxburg. J. Mach. Learn. Res., (2017)Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models.P. Rolland, V. Cevher, M. Kleindessner, C. Russell, D. Janzing, B. Schölkopf, and F. Locatello. ICML, volume 162 of Proceedings of Machine Learning Research, page 18741-18753. PMLR, (2022)Equalized odds postprocessing under imperfect group information.P. Awasthi, M. Kleindessner, and J. Morgenstern. AISTATS, volume 108 of Proceedings of Machine Learning Research, page 1770-1780. PMLR, (2020)Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information.P. Awasthi, A. Beutel, M. Kleindessner, J. Morgenstern, and X. Wang. FAccT, page 206-214. ACM, (2021)Measuring Fairness of Rankings under Noisy Sensitive Information.A. Ghazimatin, M. Kleindessner, C. Russell, Z. Abedjan, and J. Golebiowski. FAccT, page 2263-2279. ACM, (2022)

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Disambiguation of "Kleindessner, Matthäus"

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Guarantees for Spectral Clustering with Fairness Constraints.

Please choose a person to relate this publication to

Matthäus Janczyk

Matthäus Pietz

Matthäus Bartsch

Gabor Matthäus

Horst Matthäus

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Kleindessner, Matthäus"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Guarantees for Spectral Clustering with Fairness Constraints.

Please choose a person to relate this publication to

Matthäus Janczyk

Matthäus Pietz

Matthäus Bartsch

Gabor Matthäus

Horst Matthäus

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Guarantees for Spectral Clustering with Fairness Constraints.