Matthäus Kleindessner

According to our database, Matthäus Kleindessner authored at least 28 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models.

David Hoffmann, Kailash Budhathoki, Matthäus Kleindessner

CoRR, 2024

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, George Karypis

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023

When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell

Proceedings of the International Conference on Machine Learning, 2023

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations.

Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Efficient fair PCA for fair representation learning.

Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Evaluating the Fairness of Discriminative Foundation Models in Computer Vision.

Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the International Conference on Machine Learning, 2022

Individual Preference Stability for Clustering.

Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian

Proceedings of the International Conference on Machine Learning, 2022

Active Sampling for Min-Max Fairness.

Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang

Proceedings of the International Conference on Machine Learning, 2022

Measuring Fairness of Rankings under Noisy Sensitive Information.

Azin Ghazimatin, Matthäus Kleindessner, Chris Russell, Ziawasch Abedjan, Jacek Golebiowski

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Pairwise Fairness for Ordinal Regression.

Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

Frontiers Big Data, 2021

Backward-Compatible Prediction Updates: A Probabilistic Approach.

Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information.

Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

2020

Adaptive Sampling to Reduce Disparate Performance.

Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang

CoRR, 2020

A Notion of Individual Fairness for Clustering.

Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern

CoRR, 2020

Equalized odds postprocessing under imperfect group information.

Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information.

Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern

CoRR, 2019

Guarantees for Spectral Clustering with Fairness Constraints.

Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern

Proceedings of the 36th International Conference on Machine Learning, 2019

Fair k-Center Clustering for Data Summarization.

Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Crowdsourcing with Arbitrary Adversaries.

Matthäus Kleindessner, Pranjal Awasthi

Proceedings of the 35th International Conference on Machine Learning, 2018

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

Kernel functions based on triplet comparisons.

Matthäus Kleindessner, Ulrike von Luxburg

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Kernel functions based on triplet similarity comparisons.

Matthäus Kleindessner, Ulrike von Luxburg

CoRR, 2016

2015

Dimensionality estimation without distances.

Matthäus Kleindessner, Ulrike von Luxburg

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Uniqueness of Ordinal Embedding.

Matthäus Kleindessner, Ulrike von Luxburg

Proceedings of The 27th Conference on Learning Theory, 2014