June 13, 2023
D-Adaptation is an approach to automatically setting the learning rate which asymptotically achieves the optimal rate of convergence for minimizing convex Lipschitz functions, with no back-tracking or line searches, and no additional function value or gradient evaluations per step. Our approach is the first hyper-parameter free method for this class without additional multiplicative log factors in the convergence rate. We present extensive experiments for SGD and Adam variants of our method, where the method automatically matches hand-tuned learning rates across more than a dozen diverse machine learning problems, including large-scale vision and language problems. An open-source implementation is available.
Publisher
ICML
Research Topics
Core Machine Learning
July 21, 2024
Ouail Kitouni, Niklas Nolte, Samuel Pérez Díaz, Sokratis Trifinopoulos, Mike Williams
July 21, 2024
July 08, 2024
Antonio Orvieto, Lin Xiao
July 08, 2024
June 17, 2024
Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, Yaron Lipman
June 17, 2024
June 17, 2024
Neta Shaul, Uriel Singer, Ricky Chen, Matt Le, Ali Thabet, Albert Pumarola, Yaron Lipman
June 17, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models