Kleindessner, Matthäus

18 publications

TMLR 2025 A Proximal Operator for Inducing 2:4-Sparsity Jonas M. Kübler, Yu-Xiang Wang, Shoham Sabach, Navid Ansari, Matthäus Kleindessner, Kailash Budhathoki, Volkan Cevher, George Karypis

NeurIPS 2025 Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving Xinyu Wang, Jonas M. Kübler, Kailash Budhathoki, Yida Wang, Matthäus Kleindessner

AISTATS 2023 Efficient Fair PCA for Fair Representation Learning Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar

ICML 2023 When Do Minimax-Fair Learning and Empirical Risk Minimization Coincide? Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell

AISTATS 2022 Pairwise Fairness for Ordinal Regression Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell

ICML 2022 Active Sampling for Min-Max Fairness Jacob D Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang

NeurIPS 2022 Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell

ICML 2022 Individual Preference Stability for Clustering Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian

CVPR 2022 Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell

ICML 2022 Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello

NeurIPS 2021 Backward-Compatible Prediction Updates: A Probabilistic Approach Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler

AISTATS 2020 Equalized Odds Postprocessing Under Imperfect Group Information Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern

ICML 2019 Fair K-Center Clustering for Data Summarization Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern

ICML 2019 Guarantees for Spectral Clustering with Fairness Constraints Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern

NeurIPS 2017 Kernel Functions Based on Triplet Comparisons Matthäus Kleindessner, Ulrike von Luxburg

JMLR 2017 Lens Depth Function and K-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis Matthäus Kleindessner, Ulrike von Luxburg

AISTATS 2015 Dimensionality Estimation Without Distances Matthäus Kleindessner, Ulrike von Luxburg

COLT 2014 Uniqueness of Ordinal Embedding Matthäus Kleindessner, Ulrike von Luxburg