Matthäus Kleindessner | DeepAI

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∙ 02/26/2023

Efficient fair PCA for fair representation learning

We revisit the problem of fair principal component analysis (PCA), where...

0 Matthäus Kleindessner, et al. ∙

∙ 07/07/2022

Individual Preference Stability for Clustering

In this paper, we propose a natural notion of individual preference (IP)...

6 Saba Ahmadi, et al. ∙

∙ 04/09/2022

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

We show that deep neural networks that satisfy demographic parity do so ...

0 Michael Lohaus, et al. ∙

∙ 03/09/2022

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

Algorithmic fairness is frequently motivated in terms of a trade-off in ...

3 Dominik Zietlow, et al. ∙

∙ 03/08/2022

Score matching enables causal discovery of nonlinear additive noise models

This paper demonstrates how to recover causal graphs from the score of t...

2 Paul Rolland, et al. ∙

∙ 07/02/2021

Backward-Compatible Prediction Updates: A Probabilistic Approach

When machine learning systems meet real world applications, accuracy is ...

14 Frederik Träuble, et al. ∙

∙ 05/07/2021

Pairwise Fairness for Ordinal Regression

We initiate the study of fairness for ordinal regression, or ordinal cla...

0 Matthäus Kleindessner, et al. ∙

∙ 06/11/2020

Adaptive Sampling to Reduce Disparate Performance

Existing methods for reducing disparate performance of a classifier acro...

1 Jacob Abernethy, et al. ∙

∙ 06/08/2020

A Notion of Individual Fairness for Clustering

A common distinction in fair machine learning, in particular in fair cla...

0 Matthäus Kleindessner, et al. ∙

∙ 06/07/2019

Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information

Most approaches for ensuring or improving a model's fairness with respec...

0 Pranjal Awasthi, et al. ∙

∙ 01/24/2019

Guarantees for Spectral Clustering with Fairness Constraints

Given the widespread popularity of spectral clustering (SC) for partitio...

0 Matthäus Kleindessner, et al. ∙

∙ 01/24/2019

Fair k-Center Clustering for Data Summarization

In data summarization we want to choose k prototypes in order to summari...

0 Matthäus Kleindessner, et al. ∙

∙ 07/28/2016

Kernel functions based on triplet comparisons

Given only information in the form of similarity triplets "Object A is m...

0 Matthäus Kleindessner, et al. ∙

∙ 02/23/2016

Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis

In recent years it has become popular to study machine learning problems...

0 Matthäus Kleindessner, et al. ∙

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