Title: Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework . Abstract: As a seminal tool in self-supervised representation learning, contrastive learning has gained unprecedented attention in recent years. In essence, contrastive learning aims to leverage pairs of positive and negative samples for representation learning, which relates to exploiting neighborhood information in a feature space. By investigating the connection between contrastive learning and neighborhood component analysis (NCA), we provide a novel stochastic nearest neighbor viewpoint of contrastive learning and subsequently propose a series of contrastive losses that outperform the existing ones. Under our proposed framework, we show a principled way to design integrated contrastive losses that simultaneously achieve good accuracy and robustness on downstream tasks.