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cs.LG2025
Optimization Benchmark for Diffusion Models on Dynamical Systems
Fabian Schaipp
The training of diffusion models is often absent in the evaluation of new optimization techniques. In this work, we benchmark recent optimization algorithms for training a diffusio…
cs.LG2025
Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation
Robert M. Gower, Guillaume Garrigos, Nicolas Loizou +3
We provide a general convergence theorem of an idealized stochastic Polyak step size called SPS. Besides convexity, we only assume a local expected gradient bound, that include…
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…