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Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks., , , , , and . CoRR, (2020)Combining Ensembles and Data Augmentation can Harm your Calibration., , , , , , and . CoRR, (2020)Nonparametric guidance of autoencoder representations using label information., , and . J. Mach. Learn. Res., (2012)Sparse MoEs meet Efficient Ensembles., , , , , , , , , and 3 other author(s). Trans. Mach. Learn. Res., (2022)Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure., , , , , and . Trans. Mach. Learn. Res., (2022)Learning Latent Permutations with Gumbel-Sinkhorn Networks., , , and . ICLR (Poster), OpenReview.net, (2018)On Nonparametric Guidance for Learning Autoencoder Representations., , and . AISTATS, volume 22 of JMLR Proceedings, page 1073-1080. JMLR.org, (2012)Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning., , , , , , , , , and 14 other author(s). CoRR, (2021)Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach., , , , , and . CoRR, (2021)The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks., , , , , , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 9289-9299. PMLR, (2020)