Matthäus Kleindessner

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 39

Top Topics

Active Sampling

Machine Learning

Ordinal Regression

Top Venues

Publications

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. KDD (2024)
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 (2024)
Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar
Efficient fair PCA for fair representation learning. AISTATS (2023)
Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar
Efficient fair PCA for fair representation learning. CoRR (2023)
Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. ICLR (2023)
Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. CoRR (2023)
Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell
When do Minimax-fair Learning and Empirical Risk Minimization Coincide? ICML (2023)
Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. AIES (2023)
Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell
Pairwise Fairness for Ordinal Regression. AISTATS (2022)
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)
Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. ICML (2022)
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)
Azin Ghazimatin, Matthäus Kleindessner, Chris Russell, Ziawasch Abedjan, Jacek Golebiowski
Measuring Fairness of Rankings under Noisy Sensitive Information. FAccT (2022)
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 (2022)
Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
Individual Preference Stability for Clustering. CoRR (2022)
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 (2022)
Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. CoRR (2022)
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. CoRR (2022)
Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang
Active Sampling for Min-Max Fairness. ICML (2022)
Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
Individual Preference Stability for Clustering. ICML (2022)
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR (2021)
Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler
Backward-Compatible Prediction Updates: A Probabilistic Approach. CoRR (2021)
Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell
Pairwise Fairness for Ordinal Regression. CoRR (2021)
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 (2021)
Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler
Backward-Compatible Prediction Updates: A Probabilistic Approach. NeurIPS (2021)
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT (2021)
Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang
Adaptive Sampling to Reduce Disparate Performance. CoRR (2020)
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
Equalized odds postprocessing under imperfect group information. AISTATS (2020)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
A Notion of Individual Fairness for Clustering. CoRR (2020)
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
Guarantees for Spectral Clustering with Fairness Constraints. ICML (2019)
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. CoRR (2019)
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
Guarantees for Spectral Clustering with Fairness Constraints. CoRR (2019)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
Fair k-Center Clustering for Data Summarization. CoRR (2019)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
Fair k-Center Clustering for Data Summarization. ICML (2019)
Matthäus Kleindessner, Pranjal Awasthi
Crowdsourcing with Arbitrary Adversaries. ICML (2018)
Matthäus Kleindessner, Ulrike von Luxburg
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis. J. Mach. Learn. Res. 18 (2017)
Matthäus Kleindessner, Ulrike von Luxburg
Kernel functions based on triplet comparisons. NIPS (2017)
Matthäus Kleindessner, Ulrike von Luxburg
Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis. CoRR (2016)
Matthäus Kleindessner, Ulrike von Luxburg
Kernel functions based on triplet similarity comparisons. CoRR (2016)
Matthäus Kleindessner, Ulrike von Luxburg
Dimensionality estimation without distances. AISTATS (2015)
Matthäus Kleindessner, Ulrike von Luxburg
Uniqueness of Ordinal Embedding. COLT (2014)