1 citations · 1 across the 4 of their papers we have counts for
4 papers
Muon Does Not Converge on Convex Lipschitz Functions
Tetiana Parshakova, Ahmed Khaled, Michael Crawshaw +2
Muon and its variants have shown strong empirical performance in a variety of deep learning tasks. Existing convergence analyses of Muon rely on smoothness assumptions, though argu…
Last-Iterate Complexity of SGD for Convex and Smooth Stochastic Problems
Guillaume Garrigos, Daniel Cortild, Lucas Ketels +1
Most results on Stochastic Gradient Descent (SGD) in the convex and smooth setting are presented under the form of bounds on the ergodic function value gap. It is an open question…
Bias-Optimal Bounds for SGD: A Computer-Aided Lyapunov Analysis
Daniel Cortild, Lucas Ketels, Juan Peypouquet +1
The non-asymptotic analysis of Stochastic Gradient Descent (SGD) typically yields bounds that decompose into a bias term and a variance term. In this work, we focus on the bias com…
Multivariate Online Linear Regression for Hierarchical Forecasting
Massil Hihat, Guillaume Garrigos, Adeline Fermanian +1
In this paper, we consider a deterministic online linear regression model where we allow the responses to be multivariate. To address this problem, we introduce MultiVAW, a method…