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We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression (SQR), a loss function to learn all the conditional quantiles of a given target variable. These quantiles can be used to compute well-calibrated prediction intervals. To estimate epistemic uncertainty, we propose Orthonormal Certificates (OCs), a collection of diverse non-constant functions that map all training samples to zero. These certificates map out-of-distribution examples to non-zero values, signaling epistemic uncertainty. Our uncertainty estimators are computationally attractive, as they do not require ensembling or retraining deep models, and achieve state-of-the-art performance.
Author Information
Natasa Tagasovska (University of Lausanne)
David Lopez-Paz (Facebook AI Research)
More from the Same Authors
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2021 Poster: An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers »
Ramakrishna Vedantam · David Lopez-Paz · David Schwab -
2019 Poster: Learning about an exponential amount of conditional distributions »
Mohamed Ishmael Belghazi · Maxime Oquab · David Lopez-Paz -
2019 Poster: Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders »
Natasa Tagasovska · Damien Ackerer · Thibault Vatter -
2018 : Opening Remarks »
David Lopez-Paz -
2018 Workshop: Causal Learning »
Martin Arjovsky · Christina Heinze-Deml · Anna Klimovskaia · Maxime Oquab · Leon Bottou · David Lopez-Paz -
2017 Poster: Gradient Episodic Memory for Continual Learning »
David Lopez-Paz · Marc'Aurelio Ranzato -
2016 : Welcome »
David Lopez-Paz · Alec Radford · Leon Bottou -
2016 Workshop: Adversarial Training »
David Lopez-Paz · Leon Bottou · Alec Radford -
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David Lopez-Paz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio -
2013 Workshop: Randomized Methods for Machine Learning »
David Lopez-Paz · Quoc V Le · Alexander Smola -
2013 Poster: The Randomized Dependence Coefficient »
David Lopez-Paz · Philipp Hennig · Bernhard Schölkopf -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf