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What Do Neural Networks Learn When Trained With Random Labels?, , , , , , and . NeurIPS, (2020)Wasserstein Auto-Encoders., , , and . ICLR, OpenReview.net, (2018)When can unlabeled data improve the learning rate?, , , , , and . COLT, volume 99 of Proceedings of Machine Learning Research, page 1500-1518. PMLR, (2019)Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning., , and . ALT, volume 9355 of Lecture Notes in Computer Science, page 209-223. Springer, (2015)Practical and Consistent Estimation of f-Divergences., , , , and . NeurIPS, page 4072-4082. (2019)PAC-Bayes-Empirical-Bernstein Inequality., and . NIPS, page 109-117. (2013)Minimax Estimation of Kernel Mean Embeddings., , and . J. Mach. Learn. Res., (2017)Learning Disentangled Representations with Wasserstein Auto-Encoders., , and . ICLR (Workshop), OpenReview.net, (2018)Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders., , and . ICLR (Workshop), OpenReview.net, (2018)Fine-Grained Distribution-Dependent Learning Curves., , , , and . CoRR, (2022)