Stochastic Gradients under Nuisances

• URL: http://arxiv.org/abs/2508.20326v1
• Date: Thu, 28 Aug 2025 00:07:40 GMT
• Title: Stochastic Gradients under Nuisances
• Authors: Facheng Yu, Ronak Mehta, Alex Luedtke, Zaid Harchaoui,
• Abstract summary: We consider gradient algorithms for learning problems whose objectives rely on unknown parameters.<n>Our results show that while the presence of a nuisance can alter the optimum and upset the trajectory, the classical gradient algorithm may still converge under appropriate conditions.
• Score: 6.258101584466416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stochastic gradient optimization is the dominant learning paradigm for a variety of scenarios, from classical supervised learning to modern self-supervised learning. We consider stochastic gradient algorithms for learning problems whose objectives rely on unknown nuisance parameters, and establish non-asymptotic convergence guarantees. Our results show that, while the presence of a nuisance can alter the optimum and upset the optimization trajectory, the classical stochastic gradient algorithm may still converge under appropriate conditions, such as Neyman orthogonality. Moreover, even when Neyman orthogonality is not satisfied, we show that an algorithm variant with approximately orthogonalized updates (with an approximately orthogonalized gradient oracle) may achieve similar convergence rates. Examples from orthogonal statistical learning/double machine learning and causal inference are discussed.

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