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Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learning applications.
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Generative adversarial nets
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Calibrated structured prediction
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Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration
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Generative moment matching networks
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Gaussian process conditional density estimation
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Accurate uncertainties for deep learning using calibrated regression
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f-gan: Training generative neural samplers using variational divergence minimization
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Deep kernel learning
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Improved training of wasserstein gans
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On calibration of modern neural networks
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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
M. Kull, M. Perello Nieto, M. Kängsepp, T. Silva Filho, H. Song, and P. Flach · 2019
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Verified uncertainty calibration
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(f) rfcde: Random forests for conditional density estimation and functional data
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Distribution calibration for regression
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Calibrated reliable regression using maximum mean discrepancy
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Conditional density estimation tools in python and r with applications to photometric redshifts and likelihood-free cosmological inference
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Learning deep kernels for non-parametric two-sample tests
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A measure-theoretic approach to kernel conditional mean embeddings
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Fast calibrated additive quantile regression
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Maximum mean discrepancy test is aware of adversarial attacks
R. Gao, F. Liu, J. Zhang, B. Han, T. Liu, G. Niu, and M. Sugiyama · 2021
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
E. Hüllermeier and W. Waegeman · 2021
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Revisiting the calibration of modern neural networks
M. Minderer, J. Djolonga, R. Romijnders, F. Hubis, X. Zhai, N. Houlsby, D. Tran, and M. Lucic · 2021
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