407 citations · 411 across the 10 of their papers we have counts for
12 papers · 1 filter
How Edge of Stability Hinders SCAFFOLD in Federated Optimization
Anant Khandelwal, Michael Crawshaw, Mingrui Liu
In federated learning, it is well known that heterogeneous data can (in theory) slow down optimization, and much effort has been directed at designing optimization algorithms that…
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…
Non-Euclidean Gradient Descent Operates at the Edge of Stability
Rustem Islamov, Michael Crawshaw, Jeremy Cohen +1
The Edge of Stability (EoS) is a phenomenon where the sharpness (largest eigenvalue) of the Hessian approaches and then hovers near the stability threshold during gradient de…
Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension
Michael Crawshaw, Mingrui Liu
We consider the optimization problem of minimizing the logistic loss with gradient descent to train a linear model for binary classification with separable data. With a budget of $…
An Exploration of Non-Euclidean Gradient Descent: Muon and its Many Variants
Michael Crawshaw, Chirag Modi, Mingrui Liu +1
To define a steepest descent method over a neural network, we need to choose a norm for each layer, a way to aggregate these norms across layers, and whether to use normalization.…
Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
Michael Crawshaw, Blake Woodworth, Mingrui Liu
Existing analysis of Local (Stochastic) Gradient Descent for heterogeneous objectives requires stepsizes where is the communication interval, which ensures monotoni…