| Fair k-Center Clustering for Data Summarization M Kleindessner, P Awasthi, J Morgenstern International Conference on Machine Learning, 3448-3457, 2019 | 205 | 2019 |
| Guarantees for spectral clustering with fairness constraints M Kleindessner, S Samadi, P Awasthi, J Morgenstern International conference on machine learning, 3458-3467, 2019 | 201 | 2019 |
| Equalized odds postprocessing under imperfect group information P Awasthi, M Kleindessner, J Morgenstern International conference on artificial intelligence and statistics, 1770-1780, 2020 | 112 | 2020 |
| 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 | 88 | 2022 |
| Uniqueness of ordinal embedding M Kleindessner, U Luxburg Conference on Learning Theory, 40-67, 2014 | 78 | 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 | 66 | 2021 |
| 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 | 62 | 2022 |
| Active sampling for min-max fairness J Abernethy, P Awasthi, M Kleindessner, J Morgenstern, C Russell, ... arXiv preprint arXiv:2006.06879, 2020 | 57 | 2020 |
| 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 | 55 | 2022 |
| Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis M Kleindessner, U Von Luxburg Journal of Machine Learning Research 18 (58), 1-52, 2017 | 48 | 2017 |
| Kernel functions based on triplet comparisons M Kleindessner, U von Luxburg Advances in neural information processing systems 30, 2017 | 47 | 2017 |
| Dimensionality estimation without distances M Kleindessner, U Luxburg Artificial Intelligence and Statistics, 471-479, 2015 | 35 | 2015 |
| A notion of individual fairness for clustering M Kleindessner, P Awasthi, J Morgenstern arXiv preprint arXiv:2006.04960, 2020 | 33 | 2020 |
| 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 | 28 | 2021 |
| Crowdsourcing with Arbitrary Adversaries M Kleindessner, P Awasthi International Conference on Machine Learning, 2018 | 28 | 2018 |
| 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 | 24 | 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 | 20 | 2021 |
| Adaptive sampling to reduce disparate performance J Abernethy, P Awasthi, M Kleindessner, J Morgenstern, J Zhang arXiv preprint arXiv:2006.06879, 2020 | 18 | 2020 |
| Efficient fair PCA for fair representation learning M Kleindessner, M Donini, C Russell, MB Zafar International Conference on Artificial Intelligence and Statistics, 5250-5270, 2023 | 12 | 2023 |
| Individual preference stability for clustering S Ahmadi, P Awasthi, S Khuller, M Kleindessner, J Morgenstern, ... arXiv preprint arXiv:2207.03600, 2022 | 11 | 2022 |
pranjal awasthiGoogleVerified email at rutgers.edu
