  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.
