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Found 5 papers, 0 papers with code
Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing
The proposed PEF notion is definition-agnostic, meaning that any well-defined notion of fairness can be reduced to the PEF notion.
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
In this paper, we theoretically analyze the variance of sampling methods and show that, due to the composite structure of empirical risk, the variance of any sampling method can be decomposed into \textit{embedding approximation variance} in the forward stage and \textit{stochastic gradient variance} in the backward stage that necessities mitigating both types of variance to obtain faster convergence rate.
Efficient Fair Principal Component Analysis
It has been shown that dimension reduction methods such as PCA may be inherently prone to unfairness and treat data from different sensitive groups such as race, color, sex, etc., unfairly.
Semi-supervised Collaborative Ranking with Push at Top
We propose a semi-supervised collaborative ranking model, dubbed \texttt{S$^2$COR}, to improve the quality of cold-start recommendation.
Matrix Factorization with Explicit Trust and Distrust Relationships
With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences.
