Bandit-Driven Batch Selection for Robust Learning under Label Noise

Michal Lisicki, Mihai Nica, Graham W. Taylor

Keywords: bandit, batch selection, noisy labels

TL;DR: We design combinatorial bandits to training batch selection, showing that they work to filter noisy data.

Abstract: We introduce a novel approach for batch selection in Stochastic Gradient Descent (SGD) training, leveraging combinatorial bandit algorithms. Our methodology focuses on optimizing the learning process in the presence of label noise, a prevalent issue in real-world datasets. Experimental evaluations on the CIFAR-10 dataset reveal that our approach consistently outperforms existing methods across various levels of label corruption. Importantly, we achieve this superior performance without incurring the computational overhead commonly associated with auxiliary neural network models. This work presents a balanced trade-off between computational efficiency and model efficacy, offering a scalable solution for complex machine learning applications.

Submission Number: 107