Title: Lovasz Theta Contrastive Learning. Abstract: We establish a connection between the Lovasz theta function of a graph and the widely used InfoNCE loss. We show that under certain conditions, the minima of the InfoNCE loss are related to minimizing the Lovasz theta function on the empty similarity graph between the samples. Building on this connection, we generalize contrastive learning on weighted similarity graphs between samples. Our Lovasz theta contrastive loss uses a weighted graph that can be learned to take into account similarities between our data. We evaluate our method on image classification tasks, demonstrating an improvement of $1 \%$ in the supervised case and up to $4 \%$ in the unsupervised case.