humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems

• URL: http://arxiv.org/abs/2509.21254v1
• Date: Thu, 25 Sep 2025 14:46:49 GMT
• Title: humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems
• Authors: Andrii Kliachkin, Jana Lepšová, Gilles Bareilles, Jakub Mareček,
• Abstract summary: There has been a considerable interest in constrained training of deep neural networks (DNNs) for applications such as fairness and safety.<n>We present humancompatible.train, an easily-extendable PyTorch-based Python package for trainings with constraints.
• Score: 0.3499870393443268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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