We derive nearly sharp non-asymptotic error bounds for the bidirectional GAN estimators under the Dudley distance between the latent joint distribution and the data joint distribution. To the best of our knowledge, this is the first theoretical guarantee for the bidirectional GAN learning approach. An appealing feature of our results is that the reference and the target distributions are not assumed to have the same dimension or bounded support. These assumptions are commonly assumed in the exis…