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Found 14 papers, 8 papers with code
Efficient fair PCA for fair representation learning
We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic information.
Individual Preference Stability for Clustering
In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own cluster than to the points in any other cluster.
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Based on this observation, we investigate an alternative fairness approach: we add a second classification head to the network to explicitly predict the protected attribute (such as race or gender) alongside the original task.
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers
Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate.
Score matching enables causal discovery of nonlinear additive noise models
This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models.
Backward-Compatible Prediction Updates: A Probabilistic Approach
; and (ii) if the new predictions differ from the current ones, should we update?
Active Sampling for Min-Max Fairness
We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization.
A Notion of Individual Fairness for Clustering
A common distinction in fair machine learning, in particular in fair classification, is between group fairness and individual fairness.
Guarantees for Spectral Clustering with Fairness Constraints
Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al. (2017).
Fair k-Center Clustering for Data Summarization
In data summarization we want to choose $k$ prototypes in order to summarize a data set.
Kernel functions based on triplet comparisons
Given only information in the form of similarity triplets "Object A is more similar to object B than to object C" about a data set, we propose two ways of defining a kernel function on the data set.
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 in a setting of ordinal distance information rather than numerical distance measurements.
