arXiv:2602.12471v2 Announce Type: replace Abstract: 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 $T$ iterations, it was recently shown that an accelerated $1/T^2$ rate is possible by choosing a large stepsize $\eta = \Theta(\gamma^2 T)$ (where $\gamma$ is the dataset's margin) despite the resulting non-monotonicity of the loss. In this paper, we provide a tighter analysis of gradient descent for this problem when the data is two-dimensional: we show that GD with
This research provides a tighter analysis of gradient descent, building on recent findings about achieving accelerated rates in logistic regression with large stepsizes.
Improved understanding and optimization of core machine learning algorithms can lead to more efficient and faster model training, particularly relevant for resource-constrained applications or large datasets.
The paper refines the theoretical understanding of large stepsize gradient descent in specific contexts, offering potential avenues for practical algorithmic improvements in binary classification.
- · AI researchers
- · Machine learning engineers
- · Companies using logistic regression
Refined theoretical understanding of large stepsize gradient descent for logistic regression in low dimensions.
Potential for development of more robust or faster gradient descent variants for specific classification problems.
Slight acceleration in the development and deployment of certain AI models due to more efficient training.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG