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arXiv · 2505.22602

One Rank at a Time: Cascading Error Dynamics in Sequential Learning

Mahtab Alizadeh Vandchali·Fangshuo·Liao·Anastasios Kyrillidis
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Abstract

Sequential learning -- where complex tasks are broken down into simpler, hierarchical components -- has emerged as a paradigm in AI. This paper views sequential learning through the lens of low-rank linear regression, focusing specifically on how errors propagate when learning rank-1 subspaces sequentially. We present an analysis framework that decomposes the learning process into a series of rank-1 estimation problems, where each subsequent estimation depends on the accuracy of previous steps. Our contribution is a characterization of the error propagation in this sequential process, establishing bounds on how errors -- e.g., due to limited computational budgets and finite precision -- affect the overall model accuracy. We prove that these errors compound in predictable ways, with implications for both algorithmic design and stability guarantees.

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Mahtab Alizadeh Vandchali, Fangshuo, Liao, Anastasios Kyrillidis. 2025-05-28. One Rank at a Time: Cascading Error Dynamics in Sequential Learning. https://arxiv.org/abs/2505.22602

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