Decision making is a process that is extremely prone to different biases. In
this paper we consider learning fair representations that aim at removing
nuisance (sensitive) information from the decision process. For this purpose,
we propose to use deep generative modeling and adapt a hierarchical Variational
Auto-Encoder to learn these fair representations. Moreover, we utilize the
mutual information as a useful regularizer for enforcing fairness of a
representation. In experiments on two benchmark datasets and two scenarios
where the sensitive variables are fully and partially observable, we show that
the proposed approach either outperforms or performs on par with the current
best model.

本文考虑使用深层生成建模和层次化变分自动编码器，学习公正的表示并利用互信息作为有用的正则化因子来强制表示的公正性，实验证明该方法优于或与当前最佳模型持平。

分层 VampPrior 变分公平自编码器

Hierarchical VampPrior Variational Fair Auto-Encoder

Generic generation and manipulation of text is challenging and has limited
success compared to recent deep generative modeling in visual domain. This
paper aims at generating plausible natural language sentences, whose attributes
are dynamically controlled by learning disentangled latent representations with
designated semantics. We propose a new neural generative model which combines
variational auto-encoders and holistic attribute discriminators for effective
imposition of semantic structures. With differentiable approximation to
discrete text samples, explicit constraints on independent attribute controls,
and efficient collaborative learning of generator and discriminators, our model
learns highly interpretable representations from even only word annotations,
and produces realistic sentences with desired attributes. Quantitative
evaluation validates the accuracy of sentence and attribute generation.

本文旨在通过学习简化的语义分离潜在表示来生成和控制自然语言句子，同时通过变分自编码器和全局属性判别器的组合来建立新的神经生成模型。该模型具有可解释性和高效性，可以生成符合要求的具有各种属性的句子。量化评估验证了该模型的生成准确性。