Uniqueness of Ordinal Embedding. - Matthäus Kleindessner; Ulrike von Luxburg (COLT, 2014)

Dimensionality estimation without distances. - Matthäus Kleindessner; Ulrike von Luxburg (AISTATS, 2015)

Kernel functions based on triplet similarity comparisons. - Matthäus Kleindessner; Ulrike von Luxburg (CoRR, 2016)

Kernel functions based on triplet comparisons. - Matthäus Kleindessner; Ulrike von Luxburg (NIPS, 2017)

Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis. - Matthäus Kleindessner; Ulrike von Luxburg (J. Mach. Learn. Res., 2017)

Crowdsourcing with Arbitrary Adversaries. - Matthäus Kleindessner; Pranjal Awasthi (ICML, 2018)

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 (ICML, 2019)

Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. - Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern (CoRR, 2019)

Equalized odds postprocessing under imperfect group information. - Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern (AISTATS, 2020)

A Notion of Individual Fairness for Clustering. - Matthäus Kleindessner; Pranjal Awasthi; Jamie Morgenstern (CoRR, 2020)

Adaptive Sampling to Reduce Disparate Performance. - Jacob D. Abernethy; Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern; Jie Zhang 096 (CoRR, 2020)

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. - Pranjal Awasthi; Alex Beutel; Matthäus Kleindessner; Jamie Morgenstern; Xuezhi Wang 002 (FAccT, 2021)

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

Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere. - Yiyi Huang; Matthäus Kleindessner; Alexey A. Munishkin; Debvrat Varshney; Pei Guo; Jianwu Wang 001 (Frontiers Big Data, 2021)

Pairwise Fairness for Ordinal Regression. - Matthäus Kleindessner; Samira Samadi; Muhammad Bilal Zafar; Krishnaram Kenthapadi; Chris Russell 001 (AISTATS, 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 001 (CVPR, 2022)

Measuring Fairness of Rankings under Noisy Sensitive Information. - Azin Ghazimatin; Matthäus Kleindessner; Chris Russell 001; Ziawasch Abedjan; Jacek Golebiowski (FAccT, 2022)

Active Sampling for Min-Max Fairness. - Jacob D. Abernethy; Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern; Chris Russell 001; Jie Zhang 096 (ICML, 2022)

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

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. - Paul Rolland; Volkan Cevher; Matthäus Kleindessner; Chris Russell 001; Dominik Janzing; Bernhard Schölkopf; Francesco Locatello (ICML, 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 001 (NeurIPS, 2022)

Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. - Junaid Ali 001; Matthäus Kleindessner; Florian Wenzel; Kailash Budhathoki; Volkan Cevher; Chris Russell 001 (AIES, 2023)

Efficient fair PCA for fair representation learning. - Matthäus Kleindessner; Michele Donini; Chris Russell 001; Muhammad Bilal Zafar (AISTATS, 2023)

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. - Andrii Zadaianchuk; Matthäus Kleindessner; Yi Zhu; Francesco Locatello; Thomas Brox (ICLR, 2023)

When do Minimax-fair Learning and Empirical Risk Minimization Coincide? - Harvineet Singh; Matthäus Kleindessner; Volkan Cevher; Rumi Chunara; Chris Russell 001 (ICML, 2023)

Inference Optimization of Foundation Models on AI Accelerators. - Youngsuk Park; Kailash Budhathoki; Liangfu Chen; Jonas M. Kübler; Jiaji Huang; Matthäus Kleindessner; Jun Huan; Volkan Cevher; Yida Wang 003; George Karypis (KDD, 2024)

LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models. - David Hoffmann; Kailash Budhathoki; Matthäus Kleindessner (CoRR, 2024)

A Proximal Operator for Inducing 2:4-Sparsity. - Jonas M. Kübler; Yu-Xiang Wang 003; Shoham Sabach; Navid Ansari; Matthäus Kleindessner; Kailash Budhathoki; Volkan Cevher; George Karypis (CoRR, 2025)