Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
This addresses the challenge of enhancing optimization efficiency and generalization across various learning tasks for machine learning practitioners, representing a novel method rather than an incremental improvement.
The paper tackles the problem of improving deep neural network performance by introducing Re-weighted Gradient Descent (RGD), a novel optimization technique that dynamically re-weights training samples based on distributionally robust optimization, achieving state-of-the-art results with improvements such as +0.7% on DomainBed and +1.94% on GLUE with BERT.
We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample re-weighting. Leveraging insights from distributionally robust optimization (DRO) with Kullback-Leibler divergence, our method dynamically assigns importance weights to training data during each optimization step. RGD is simple to implement, computationally efficient, and compatible with widely used optimizers such as SGD and Adam. We demonstrate the effectiveness of RGD on various learning tasks, including supervised learning, meta-learning, and out-of-domain generalization. Notably, RGD achieves state-of-the-art results on diverse benchmarks, with improvements of +0.7% on DomainBed, +1.44% on tabular classification, \textcolor{blue}+1.94% on GLUE with BERT, and +1.01% on ImageNet-1K with ViT.