# Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization Source: [Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:29:07.731Z Authors: Ramnath Kumar, Kushal Majmundar, Dheeraj Nagaraj, Arun Sai Suggala Published: 2023-06-15T15:58:04.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()