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[2507.04033] Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks
Summary
This paper benchmarks stochastic approximation algorithms for fairness-constrained training of deep neural networks, addressing theoretical challenges and comparing optimization performance.
Why It Matters
As machine learning models increasingly impact society, ensuring fairness in their training is crucial. This paper provides a benchmark for evaluating algorithms aimed at improving fairness, contributing to the development of more equitable AI systems.
Key Takeaways
- The paper presents a benchmark for fairness-constrained learning tasks using real-world data.
- It highlights theoretical challenges in training deep neural networks with fairness constraints.
- Three new stochastic approximation algorithms are compared for optimization performance and fairness improvement.
- The benchmark code is released as a Python package for wider accessibility.
- The study emphasizes the need for standardized methods in fairness-constrained training.
Computer Science > Machine Learning arXiv:2507.04033 (cs) [Submitted on 5 Jul 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks Authors:Andrii Kliachkin, Jana Lepšová, Gilles Bareilles, Jakub Mareček View a PDF of the paper titled Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks, by Andrii Kliachkin and 3 other authors View PDF HTML (experimental) Abstract:The ability to train Deep Neural Networks (DNNs) with constraints is instrumental in improving the fairness of modern machine-learning models. Many algorithms have been analysed in recent years, and yet there is no standard, widely accepted method for the constrained training of DNNs. In this paper, we provide a challenging benchmark of real-world large-scale fairness-constrained learning tasks, built on top of the US Census (Folktables). We point out the theoretical challenges of such tasks and review the main approaches in stochastic approximation algorithms. Finally, we demonstrate the use of the benchmark by implementing and comparing three recently proposed, but as-of-yet unimplemented, algorithms both in terms of optimization performance, and fairness improvement. We release the code of the benchmark as a Python package at this https URL. Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY); Optimization and Control (math.OC); M...
