Supplementary Material Appendix T able of Contents

CIFAR10 dataset is released with an MIT license. All the datasets used in this work are publicly available. We begin by first stating and then proving Theorem 1. Theorem We introduce the notations used in the proof of the theorem in subsection B.2. Here, we discuss some prior works on submodularity. Detailed description of SSL loss formulation for different SSL algorithms are given below: C.2 Mean-T eacher Mean Teacher [56] proposed to generate a more stable target output for data points in the unlabeled C.5 FixMatch FixMatch [53] uses the cross-entropy loss between class predictions of weak augmented and strong We used various standard datasets, viz., CIFAR10, SVHN, to demonstrate the effectiveness and The descriptions of the datasets used along with the licenses are given in the Table 1. We adapt it to SSL by choosing a representative subset of unlabeled points such that the gradients are similar to the unlabeled loss gradients.