Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting
DOI:
https://doi.org/10.1609/aaai.v36i6.20573Keywords:
Machine Learning (ML)Abstract
Parameter-free stochastic gradient descent (PFSGD) algorithms do not require setting learning rates while achieving optimal theoretical performance. In practical applications, however, there remains an empirical gap between tuned stochastic gradient descent (SGD) and PFSGD. In this paper, we close the empirical gap with a new parameter-free algorithm based on continuous-time Coin-Betting on truncated models. The new update is derived through the solution of an Ordinary Differential Equation (ODE) and solved in a closed form. We show empirically that this new parameter-free algorithm outperforms algorithms with the ``best default'' learning rates and almost matches the performance of finely tuned baselines without anything to tune.Downloads
Published
2022-06-28
How to Cite
Chen, K., Langford, J., & Orabona, F. (2022). Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6239-6247. https://doi.org/10.1609/aaai.v36i6.20573
Issue
Section
AAAI Technical Track on Machine Learning I