开源工具包实现随机约束下的深度学习优化，助力公平性训练。
humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems
- 基于PyTorch构建可扩展框架，支持随机约束优化
- 首次实现多个未公开的随机约束优化算法
- 适合关注模型公平性与安全性的研究者使用
近年来，深度神经网络（DNN）的约束训练在公平性与安全性等应用中受到广泛关注。尽管已有若干工具包被提出，但尚未形成行业标准。本文介绍 humancompatible.train（https://github.com/humancompatible/train），一个基于PyTorch的可扩展Python包，用于在随机约束下训练DNN。该工具包实现了多个此前未被实现的随机约束随机优化算法，并通过在公平性约束下的深度学习任务中对比两种算法，展示了其实际应用价值。
原文摘要 · Abstract (English)
There has been a considerable interest in constrained training of deep neural networks (DNNs) recently for applications such as fairness and safety. Several toolkits have been proposed for this task, yet there is still no industry standard. We present humancompatible.train (https://github.com/humancompatible/train), an easily-extendable PyTorch-based Python package for training DNNs with stochastic constraints. We implement multiple previously unimplemented algorithms for stochastically constrained stochastic optimization. We demonstrate the toolkit use by comparing two algorithms on a deep learning task with fairness constraints.
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