Title: Generative Gradual Domain Adaptation with Optimal Transport. Abstract: Unsupervised domain adaptation (UDA) adapts a model from a labeled source domain to an unlabeled target domain in a one-off way. Though widely applied, UDA faces a great challenge whenever the distribution shift between the source and the target is large. Gradual domain adaptation (GDA) mitigates this limitation by using intermediate domains to gradually adapt from the source to the target domain. However, it remains an open problem on how to leverage this paradigm when the oracle intermediate domains are missing or scarce. To approach this practical challenge, we propose Generative Gradual Domain Adaptation with Optimal Transport (GOAT), an algorithmic framework that can generate intermediate domains in a data-dependent way. More concretely, we generate intermediate domains along the Wasserstein geodesic between two given consecutive domains in a feature space, and apply gradual self-training, a standard GDA algorithm, to adapt the source-trained classifier to the target along the sequence of intermediate domains. Empirically, we demonstrate that our GOAT framework can improve the performance of standard GDA when the oracle intermediate domains are scarce, significantly broadening the real-world application scenarios of GDA.