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This work introduces an efficient novel approach for epistemic uncertainty estimation for ensemble models for regression tasks using pairwise-distance estimators (PaiDEs).
Information-based objective functions for active data selection
David JC MacKay · 1992
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Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management
Stephen C Hora · 1996
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Mutual information, metric entropy and cumulative relative entropy risk
David Haussler and Manfred Opper · 1997
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Elements of information theory
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Approximating the kullback leibler divergence between gaussian mixture models
John R Hershey and Peder A Olsen · 2007
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On entropy approximation for gaussian mixture random vectors
Marco F Huber, Tim Bailey, Hugh Durrant-Whyte, and Uwe D Hanebeck · 2008
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Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2009
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How to deal with the curse of dimensionality of likelihood ratios in monte carlo simulation
Reuven Y Rubinstein and Peter W Glynn · 2009
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Active learning literature survey
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Two useful bounds for variational inference
John William Paisley · 2010
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Pattern classification using ensemble methods , volume 75
Lior Rokach · 2010
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Density estimation by dual ascent of the log-likelihood
Esteban G Tabak and Eric Vanden-Eijnden · 2010
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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Chernoff information of exponential families
Frank Nielsen · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Andrey Malinin and Mark Gales · 2020
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The hidden uncertainty in a neural networks activations
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Uncertainty estimation using a single deep deterministic neural network
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Gone fishing: Neural active learning with fisher embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, and Sham Kakade · 2021
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Estimating mixture entropy with pairwise distances
Artemy Kolchinsky and Brendan D Tracey · 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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Waic, but why? generative ensembles for robust anomaly detection
Hyunsun Choi, Eric Jang, and Alexander A Alemi · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
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Glow: Generative flow with invertible 1x1 convolutions
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Natural posterior network: Deep bayesian uncertainty for exponential family distributions
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nflows: normalizing flows in PyTorch
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Estimating and evaluating regression predictive uncertainty in deep object detectors
Ali Harakeh and Steven L Waslander · 2021
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
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Combining social and intrinsically-motivated learning for multi-task robot skill acquisition
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Active learning of bayesian probabilistic movement primitives
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric T Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models
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Marginal tail-adaptive normalizing flows
Mike Laszkiewicz, Johannes Lederer, and Asja Fischer · 2022
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Normalizing flow ensembles for rich aleatoric and epistemic uncertainty modeling
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Distributionally robust statistical verification with imprecise neural networks
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