Artificial neural nets can represent and classify many types of data but are
often tailored to particular applications -- e.g., for "fair" or "hierarchical"
classification. Once an architecture has been selected, it is often difficult
for humans to adjust models for a new task; for example, a hierarchical
classifier cannot be easily transformed into a fair classifier that shields a
protected field. Our contribution in this work is a new neural network
architecture, the concept subspace network (CSN), which generalizes existing
specialized classifiers to produce a unified model capable of learning a
spectrum of multi-concept relationships. We demonstrate that CSNs reproduce
state-of-the-art results in fair classification when enforcing concept
independence, may be transformed into hierarchical classifiers, or even
reconcile fairness and hierarchy within a single classifier. The CSN is
inspired by existing prototype-based classifiers that promote interpretability.

本文提出了一种新的神经网络结构 —— 概念子空间网络 (CSN)，能够将现有专门的分类器推广到产生一个统一模型，能够学习一系列的多概念关系。在实现概念独立性的情况下，我们证明了 CSN 在公平分类方面能够复现最先进的结果，可以被转变为分层分类器，甚至可以在单个分类器内调和公平性和层次性。

基于原型的分类：从层级到公平

Prototype Based Classification from Hierarchy to Fairness

We propose a general framework for increasing local stability of Artificial
Neural Nets (ANNs) using Robust Optimization (RO). We achieve this through an
alternating minimization-maximization procedure, in which the loss of the
network is minimized over perturbed examples that are generated at each
parameter update. We show that adversarial training of ANNs is in fact
robustification of the network optimization, and that our proposed framework
generalizes previous approaches for increasing local stability of ANNs.
Experimental results reveal that our approach increases the robustness of the
network to existing adversarial examples, while making it harder to generate
new ones. Furthermore, our algorithm improves the accuracy of the network also
on the original test data.

提出了一个使用鲁棒性优化（RO）增加人工神经网络（ANN）本地稳定性的通用框架。该算法是通过替代最小化 - 最大化过程实现的，该网络的损失是在每个参数更新时生成的扰动样本上最小化的。实验结果表明，该方法提高了网络对现有敌对示例的鲁棒性，同时使生成新的敌对示例更加困难，而且该算法还提高了网络在原始测试数据上的准确性。