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Found 2 papers, 1 papers with code
Fair and Useful Cohort Selection
A challenge in fair algorithm design is that, while there are compelling notions of individual fairness, these notions typically do not satisfy desirable composition properties, and downstream applications based on fair classifiers might not preserve fairness.
Factorized Graph Representations for Semi-Supervised Learning from Sparse Data
We answer this question affirmatively and suggest a method called distant compatibility estimation that works even on extremely sparsely labeled graphs (e. g., 1 in 10, 000 nodes is labeled) in a fraction of the time it later takes to label the remaining nodes.
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