Learning Fair Representation via Distributional Contrastive Disentanglement. (arXiv:2206.08743v1 [cs.LG])

Learning fair representation is crucial for achieving fairness or debiasing
sensitive information. Most existing works rely on adversarial representation
learning to inject some invariance into representation. However, adversarial
learning methods are known to suffer from relatively unstable training, and
this might harm the balance between fairness and predictiveness of
representation. We propose a new approach, learning FAir Representation via
distributional CONtrastive Variational AutoEncoder (FarconVAE), which induces
the latent space to be disentangled into sensitive and nonsensitive parts. We
first construct …

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