6 papers
Adaptive Sharpness-Aware Minimization with a Polyak-type Step size: A Theory-Grounded Scheduler
Dimitris Oikonomou, Nicolas Loizou
Sharpness-Aware Minimization (SAM) has established itself as a powerful and widely adopted optimizer for training machine learning models. By explicitly minimizing the sharpness of…
Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients
Dimitris Oikonomou, Nicolas Loizou
The stochastic Polyak step size (SPS) has proven to be a promising choice for stochastic gradient descent (SGD), delivering competitive performance relative to state-of-the-art met…
Taking the Road Less Scheduled with Adaptive Polyak Steps
Dimitris Oikonomou, Matthew Buchholz, Yuen-Man Pun +2
Schedule-Free SGD, proposed in [Defazio et al., 2024], achieves optimal convergence rates without requiring the training horizon in advance, by replacing learning rate schedules wi…
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…
Sharpness-Aware Minimization: General Analysis and Improved Rates
Dimitris Oikonomou, Nicolas Loizou
Sharpness-Aware Minimization (SAM) has emerged as a powerful method for improving generalization in machine learning models by minimizing the sharpness of the loss landscape. Howev…
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance
Dimitris Oikonomou, Nicolas Loizou
Stochastic gradient descent with momentum, also known as Stochastic Heavy Ball method (SHB), is one of the most popular algorithms for solving large-scale stochastic optimization p…