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Accurate quantification of uncertainty is crucial for real-world applications of machine learning.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
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The well-calibrated Bayesian
A Philip Dawid · 1982
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The comparison and evaluation of forecasters
Morris H DeGroot and Stephen E Fienberg · 1983
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A practical Bayesian framework for backpropagation networks
David JC MacKay · 1992
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Mixture density networks
Christopher M Bishop · 1994
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Estimating the mean and variance of the target probability distribution
David A Nix and Andreas S Weigend · 1994
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Kernel methods in machine learning
Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola · 2008
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Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2009
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Optimal kernel choice for large-scale two-sample tests
Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, and Bharath K Sriperumbudur · 2012
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Deep Gaussian processes
Andreas Damianou and Neil Lawrence · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
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Obtaining well calibrated probabilities using Bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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A better measure of relative prediction accuracy for model selection and model estimation
Chris Tofallis · 2015
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Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
Doubly stochastic variational inference for deep gaussian processes
Hugh Salimbeni and Marc Deisenroth · 2017
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Deep and confident prediction for time series at Uber
Lingxue Zhu and Nikolay Laptev · 2017
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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
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High-quality prediction intervals for deep learning: A distribution-free, ensembled approach
Tim Pearce, Alexandra Brintrup, Mohamed Zaki, and Andy Neely · 2018
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Kernel implicit variational inference
Jiaxin Shi, Shengyang Sun, and Jun Zhu · 2018
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Uncertainty in Deep Learning
Yarin Gal · 2016
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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What uncertainties do we need in Bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Leveraging uncertainty information from deep neural networks for disease detection
Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl · 2017
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Generative well-intentioned networks
Justin Cosentino and Jun Zhu · 2019
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Distribution calibration for regression
Hao Song, Tom Diethe, Meelis Kull, and Peter Flach · 2019
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Building calibrated deep models via uncertainty matching with auxiliary interval predictors
Jayaraman J Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, and Peer-Timo Bremer · 2019
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Function space particle optimization for bayesian neural networks
Ziyu Wang, Tongzheng Ren, Jun Zhu, and Bo Zhang · 2019
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https://www.kaggle.com/yannisp/uber-pickups-enriched
Nyc uber pickups with weather and holidays | kaggle · 2020
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http://archive.ics.uci.edu/ml
UCI machine learning repository · 2020
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Uncertainty quantification with statistical guarantees in end-to-end autonomous driving control
Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, and Marta Kwiatkowska · 2020
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