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Matthäus Kleindessner
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2020 – today
- 2024
- [c21]Youngsuk Park
, Kailash Budhathoki
Inference Optimization of Foundation Models on AI Accelerators. KDD 2024: 6605-6615 - [i19]Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis:
Inference Optimization of Foundation Models on AI Accelerators. CoRR abs/2407.09111 (2024) - [i18]David Hoffmann, Kailash Budhathoki, Matthäus Kleindessner:
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models. CoRR abs/2410.13299 (2024) - 2023
- [c20]Junaid Ali
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. AIES 2023: 809-833 - [c19]Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar:
Efficient fair PCA for fair representation learning. AISTATS 2023: 5250-5270 - [c18]Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox:
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. ICLR 2023 - [c17]Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell:
When do Minimax-fair Learning and Empirical Risk Minimization Coincide? ICML 2023: 31969-31989 - [i17]Matthäus Kleindessner, Michele Donini, Chris Russell
Efficient fair PCA for fair representation learning. CoRR abs/2302.13319 (2023) - [i16]Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell:
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. CoRR abs/2310.11867 (2023) - 2022
- [c16]Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell:
Pairwise Fairness for Ordinal Regression. AISTATS 2022: 3381-3417 - [c15]Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CVPR 2022: 10400-10411 - [c14]Azin Ghazimatin, Matthäus Kleindessner, Chris Russell
Measuring Fairness of Rankings under Noisy Sensitive Information. FAccT 2022: 2263-2279 - [c13]Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang:
Active Sampling for Min-Max Fairness. ICML 2022: 53-65 - [c12]Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
Individual Preference Stability for Clustering. ICML 2022: 197-246 - [c11]Paul Rolland, Volkan Cevher
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. ICML 2022: 18741-18753 - [c10]Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell:
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. NeurIPS 2022 - [i15]Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello:
Score matching enables causal discovery of nonlinear additive noise models. CoRR abs/2203.04413 (2022) - [i14]Dominik Zietlow, Michael Lohaus, Guha Balakrishnan
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CoRR abs/2203.04913 (2022) - [i13]Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. CoRR abs/2204.04440 (2022) - [i12]Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
Individual Preference Stability for Clustering. CoRR abs/2207.03600 (2022) - [i11]Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox:
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. CoRR abs/2207.05027 (2022) - 2021
- [j2]Yiyi Huang, Matthäus Kleindessner, Alexey A. Munishkin, Debvrat Varshney, Pei Guo, Jianwu Wang
Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere. Frontiers Big Data 4: 642182 (2021) - [c9]Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT 2021: 206-214 - [c8]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. NeurIPS 2021: 116-128 - [i10]Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR abs/2102.08410 (2021) - [i9]Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell:
Pairwise Fairness for Ordinal Regression. CoRR abs/2105.03153 (2021) - [i8]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. CoRR abs/2107.01057 (2021) - 2020
- [c7]Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern:
Equalized odds postprocessing under imperfect group information. AISTATS 2020: 1770-1780 - [i7]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
A Notion of Individual Fairness for Clustering. CoRR abs/2006.04960 (2020) - [i6]Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang:
Adaptive Sampling to Reduce Disparate Performance. CoRR abs/2006.06879 (2020)
2010 – 2019
- 2019
- [c6]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
Fair k-Center Clustering for Data Summarization. ICML 2019: 3448-3457 - [c5]Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern:
Guarantees for Spectral Clustering with Fairness Constraints. ICML 2019: 3458-3467 - [i5]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
Fair k-Center Clustering for Data Summarization. CoRR abs/1901.08628 (2019) - [i4]Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern:
Guarantees for Spectral Clustering with Fairness Constraints. CoRR abs/1901.08668 (2019) - [i3]Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern:
Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. CoRR abs/1906.03284 (2019) - 2018
- [c4]Matthäus Kleindessner, Pranjal Awasthi:
Crowdsourcing with Arbitrary Adversaries. ICML 2018: 2713-2722 - 2017
- [j1]Matthäus Kleindessner, Ulrike von Luxburg:
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis. J. Mach. Learn. Res. 18: 58:1-58:52 (2017) - [c3]Matthäus Kleindessner, Ulrike von Luxburg:
Kernel functions based on triplet comparisons. NIPS 2017: 6807-6817 - 2016
- [i2]Matthäus Kleindessner, Ulrike von Luxburg:
Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis. CoRR abs/1602.07194 (2016) - [i1]Matthäus Kleindessner, Ulrike von Luxburg:
Kernel functions based on triplet similarity comparisons. CoRR abs/1607.08456 (2016) - 2015
- [c2]Matthäus Kleindessner, Ulrike von Luxburg:
Dimensionality estimation without distances. AISTATS 2015 - 2014
- [c1]Matthäus Kleindessner, Ulrike von Luxburg:
Uniqueness of Ordinal Embedding. COLT 2014: 40-67
Coauthor Index
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