Matthäus Kleindessner
Matthäus Kleindessner
Amazon AWS Tübingen
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Cited by
Cited by
Fair k-Center Clustering for Data Summarization
M Kleindessner, P Awasthi, J Morgenstern
International Conference on Machine Learning, 3448-3457, 2019
Guarantees for spectral clustering with fairness constraints
M Kleindessner, S Samadi, P Awasthi, J Morgenstern
International Conference on Machine Learning, 3458-3467, 2019
Equalized odds postprocessing under imperfect group information
P Awasthi, M Kleindessner, J Morgenstern
International conference on artificial intelligence and statistics, 1770-1780, 2020
Uniqueness of ordinal embedding
M Kleindessner, U Luxburg
Conference on Learning Theory, 40-67, 2014
Evaluating fairness of machine learning models under uncertain and incomplete information
P Awasthi, A Beutel, M Kleindessner, J Morgenstern, X Wang
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021
Score matching enables causal discovery of nonlinear additive noise models
P Rolland, V Cevher, M Kleindessner, C Russell, D Janzing, B Schölkopf, ...
International Conference on Machine Learning, 18741-18753, 2022
Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis
M Kleindessner, U Von Luxburg
The Journal of Machine Learning Research 18 (1), 1889-1940, 2017
Kernel functions based on triplet comparisons
M Kleindessner, U von Luxburg
Advances in neural information processing systems 30, 2017
Active sampling for min-max fairness
J Abernethy, P Awasthi, M Kleindessner, J Morgenstern, C Russell, ...
arXiv preprint arXiv:2006.06879, 2020
Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers
D Zietlow, M Lohaus, G Balakrishnan, M Kleindessner, F Locatello, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Dimensionality estimation without distances
M Kleindessner, U Luxburg
Artificial Intelligence and Statistics, 471-479, 2015
Unsupervised semantic segmentation with self-supervised object-centric representations
A Zadaianchuk, M Kleindessner, Y Zhu, F Locatello, T Brox
arXiv preprint arXiv:2207.05027, 2022
A notion of individual fairness for clustering
M Kleindessner, P Awasthi, J Morgenstern
arXiv preprint arXiv:2006.04960, 2020
Crowdsourcing with Arbitrary Adversaries
M Kleindessner, P Awasthi
International Conference on Machine Learning, 2018
Benchmarking of data-driven causality discovery approaches in the interactions of Arctic sea ice and atmosphere
Y Huang, M Kleindessner, A Munishkin, D Varshney, P Guo, J Wang
Frontiers in Big Data 4, 2021
Adaptive sampling to reduce disparate performance
J Abernethy, P Awasthi, M Kleindessner, J Morgenstern, J Zhang
arXiv preprint arXiv:2006.06879, 2020
Measuring fairness of rankings under noisy sensitive information
A Ghazimatin, M Kleindessner, C Russell, Z Abedjan, J Golebiowski
Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022
Pairwise fairness for ordinal regression
M Kleindessner, S Samadi, MB Zafar, K Kenthapadi, C Russell
International Conference on Artificial Intelligence and Statistics, 3381-3417, 2022
Backward-compatible prediction updates: A probabilistic approach
F Träuble, J Von Kügelgen, M Kleindessner, F Locatello, B Schölkopf, ...
Advances in Neural Information Processing Systems 34, 116-128, 2021
Individual preference stability for clustering
S Ahmadi, P Awasthi, S Khuller, M Kleindessner, J Morgenstern, ...
arXiv preprint arXiv:2207.03600, 2022
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