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cs.LG2026
Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features
Noé Colomban, Francis Bach, Fabian Schaipp
We study the last iterate of stochastic gradient descent (SGD) on streaming least squares with discretely distributed features (Slock's model), which decouples the dynamics across…
cs.LG2025
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
Fabian Schaipp, Alexander Hägele, Adrien Taylor +2
We show that learning-rate schedules for large model training behave surprisingly similar to a performance bound from non-smooth convex optimization theory. We provide a bound for…