4 papers
Understanding Self-Supervised Learning via Gaussian Mixture Models
Parikshit Bansal, Ali Kavis, Sujay Sanghavi
Self-supervised learning attempts to learn representations from un-labeled data; it does so via a loss function that encourages the embedding of a point to be close to that of its…
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
Sunny Sanyal, Hayden Prairie, Rudrajit Das +2
Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as "catastrophic forgetting". This is especially an issue when one…
Online Learning-guided Learning Rate Adaptation via Gradient Alignment
Ruichen Jiang, Ali Kavis, Aryan Mokhtari
The performance of an optimizer on large-scale deep learning models depends critically on fine-tuning the learning rate, often requiring an extensive grid search over base learning…
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
Ruichen Jiang, Ali Kavis, Qiujiang Jin +2
We propose adaptive, line search-free second-order methods with optimal rate of convergence for solving convex-concave min-max problems. By means of an adaptive step size, our algo…