Title: Learning to Prove Theorems by Learning to Generate Theorems. Abstract: We consider the task of automated theorem proving, a key AI task. Deep learning has shown promise for training theorem provers, but there are limited human-written theorems and proofs available for supervised learning. To address this limitation, we propose to learn a neural generator that automatically synthesizes theorems and proofs for the purpose of training a theorem prover. Experiments on real-world  tasks demonstrate that synthetic data from our approach significantly improves the theorem prover and advances the state of the art of automated theorem proving in Metamath.