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mixup: Beyond Empirical Risk Minimization., , , and . ICLR (Poster), OpenReview.net, (2018)Unifying distillation and privileged information., , , and . ICLR (Poster), (2016)First-Order Adversarial Vulnerability of Neural Networks and Input Dimension., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5809-5817. PMLR, (2019)Manifold Mixup: Better Representations by Interpolating Hidden States., , , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 6438-6447. PMLR, (2019)Non-linear Causal Inference using Gaussianity Measures., , , and . J. Mach. Learn. Res., (2016)No Regret Bound for Extreme Bandits., , and . AISTATS, volume 51 of JMLR Workshop and Conference Proceedings, page 259-267. JMLR.org, (2016)Single-Model Uncertainties for Deep Learning., and . NeurIPS, page 6414-6425. (2019)No Regret Bound for Extreme Bandits., , and . CoRR, (2015)Adversarial Vulnerability of Neural Networks Increases With Input Dimension., , , , and . CoRR, (2018)Discovering Causal Signals in Images., , , , and . CoRR, (2016)