Matthäus Kleindessner

46 papers A* 13A 4C 1Journal 25Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	When LLMs get significantly worse: A statistical approach to detect model degradations. CoRR Jonas M. Kübler, Kailash Budhathoki, Matthäus Kleindessner, Xiong Zhou, Junming Yin, Ashish Khetan, George Karypis
2025	J	`jnl`	A Proximal Operator for Inducing 2:4-Sparsity. CoRR Jonas M. Kübler, Yu-Xiang Wang, Shoham Sabach, Navid Ansari, Matthäus Kleindessner, Kailash Budhathoki, Volkan Cevher, George Karypis
2025	J	`jnl`	A Proximal Operator for Inducing 2:4-Sparsity. Trans. Mach. Learn. Res. Jonas M. Kübler, Yu-Xiang Wang, Shoham Sabach, Navid Ansari, Matthäus Kleindessner, Kailash Budhathoki, Volkan Cevher, George Karypis
2025	J	`jnl`	Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving. CoRR Xinyu Wang, Jonas M. Kübler, Kailash Budhathoki, Yida Wang, Matthäus Kleindessner
2024	A*	`conf`	Inference Optimization of Foundation Models on AI Accelerators. KDD Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis
2024	J	`jnl`	Inference Optimization of Foundation Models on AI Accelerators. CoRR Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis
2024	J	`jnl`	LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models. CoRR David Hoffmann, Kailash Budhathoki, Matthäus Kleindessner
2023	A	`conf`	Efficient fair PCA for fair representation learning. AISTATS Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar
2023	J	`jnl`	Efficient fair PCA for fair representation learning. CoRR Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar
2023	C	`conf`	Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. AIES Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell
2023	J	`jnl`	Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. CoRR Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell
2023	A*	`conf`	Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. ICLR Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox
2023	A*	`conf`	When do Minimax-fair Learning and Empirical Risk Minimization Coincide? ICML Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell
2022	A*	`conf`	Active Sampling for Min-Max Fairness. ICML Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang
2022	A*	`conf`	Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. NeurIPS Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell
2022	J	`jnl`	Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. CoRR Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell
2022	A*	`conf`	Individual Preference Stability for Clustering. ICML Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
2022	J	`jnl`	Individual Preference Stability for Clustering. CoRR Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
2022	A*	`conf`	Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CVPR Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell
2022	J	`jnl`	Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CoRR Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell
2022	—	`conf`	Measuring Fairness of Rankings under Noisy Sensitive Information. FAccT Azin Ghazimatin, Matthäus Kleindessner, Chris Russell, Ziawasch Abedjan, Jacek Golebiowski
2022	A	`conf`	Pairwise Fairness for Ordinal Regression. AISTATS Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell
2022	A*	`conf`	Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. ICML Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello
2022	J	`jnl`	Score matching enables causal discovery of nonlinear additive noise models. CoRR Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello
2022	J	`jnl`	Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. CoRR Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox
2021	A*	`conf`	Backward-Compatible Prediction Updates: A Probabilistic Approach. NeurIPS Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler
2021	J	`jnl`	Backward-Compatible Prediction Updates: A Probabilistic Approach. CoRR Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler
2021	J	`jnl`	Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere. Frontiers Big Data Yiyi Huang, Matthäus Kleindessner, Alexey A. Munishkin, Debvrat Varshney, Pei Guo, Jianwu Wang
2021	—	`conf`	Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
2021	J	`jnl`	Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
2021	J	`jnl`	Pairwise Fairness for Ordinal Regression. CoRR Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell
2020	J	`jnl`	A Notion of Individual Fairness for Clustering. CoRR Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
2020	J	`jnl`	Adaptive Sampling to Reduce Disparate Performance. CoRR Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang
2020	A	`conf`	Equalized odds postprocessing under imperfect group information. AISTATS Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
2019	J	`jnl`	Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. CoRR Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
2019	A*	`conf`	Fair k-Center Clustering for Data Summarization. ICML Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
2019	J	`jnl`	Fair k-Center Clustering for Data Summarization. CoRR Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
2019	A*	`conf`	Guarantees for Spectral Clustering with Fairness Constraints. ICML Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
2019	J	`jnl`	Guarantees for Spectral Clustering with Fairness Constraints. CoRR Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
2018	A*	`conf`	Crowdsourcing with Arbitrary Adversaries. ICML Matthäus Kleindessner, Pranjal Awasthi
2017	—	`conf`	Kernel functions based on triplet comparisons. NIPS Matthäus Kleindessner, Ulrike von Luxburg
2017	J	`jnl`	Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis. J. Mach. Learn. Res. Matthäus Kleindessner, Ulrike von Luxburg
2016	J	`jnl`	Kernel functions based on triplet similarity comparisons. CoRR Matthäus Kleindessner, Ulrike von Luxburg
2016	J	`jnl`	Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis. CoRR Matthäus Kleindessner, Ulrike von Luxburg
2015	A	`conf`	Dimensionality estimation without distances. AISTATS Matthäus Kleindessner, Ulrike von Luxburg
2014	A*	`conf`	Uniqueness of Ordinal Embedding. COLT Matthäus Kleindessner, Ulrike von Luxburg