2 citations · 2 across the 3 of their papers we have counts for
3 papers
math.OC2026
Free Heavy-Tailed Lunch for Muon: A Theoretical Justification of Empirical Success
Florian Hübler, Thomas Pethick, Suvrit Sra
Non-Euclidean optimisation methods with matrix-valued updates, such as Muon and Scion, have recently shown strong empirical performance for training Transformer models, yet their t…
math.OC2025
Can SGD Handle Heavy-Tailed Noise?
Ilyas Fatkhullin, Florian Hübler, Guanghui Lan
Stochastic Gradient Descent (SGD) is a cornerstone of large-scale optimization, yet its theoretical behavior under heavy-tailed noise -- common in modern machine learning and reinf…
math.OC2024★ 2 cited
From Gradient Clipping to Normalization for Heavy Tailed SGD
Florian Hübler, Ilyas Fatkhullin, Niao He
Recent empirical evidence indicates that many machine learning applications involve heavy-tailed gradient noise, which challenges the standard assumptions of bounded variance in st…