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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...
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07/07/2022
Individual Preference Stability for Clustering
In this paper, we propose a natural notion of individual preference (IP)...
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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 ...
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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 ...
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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...
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07/02/2021
Backward-Compatible Prediction Updates: A Probabilistic Approach
When machine learning systems meet real world applications, accuracy is ...
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05/07/2021
Pairwise Fairness for Ordinal Regression
We initiate the study of fairness for ordinal regression, or ordinal cla...
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06/11/2020
Adaptive Sampling to Reduce Disparate Performance
Existing methods for reducing disparate performance of a classifier acro...
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06/08/2020
A Notion of Individual Fairness for Clustering
A common distinction in fair machine learning, in particular in fair cla...
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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...
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01/24/2019
Guarantees for Spectral Clustering with Fairness Constraints
Given the widespread popularity of spectral clustering (SC) for partitio...
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01/24/2019
Fair k-Center Clustering for Data Summarization
In data summarization we want to choose k prototypes in order to summari...
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07/28/2016
Kernel functions based on triplet comparisons
Given only information in the form of similarity triplets "Object A is m...
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02/23/2016