Fetching the paper…
Reading the bibliography…
Effective decision making requires understanding the uncertainty inherent in a prediction.
Decision-making in a fuzzy environment
Richard E Bellman and Lotfi Asker Zadeh · 1970
Earlier work this paper cites.
Estimating regression models with multiplicative heteroscedasticity
Andrew C Harvey · 1976
Earlier work this paper cites.
Regression quantiles
R Koenker and G Bassett · 1978
Earlier work this paper cites.
Estimating the mean and variance of the target probability distribution
David A Nix and Andreas S Weigend · 1994
Earlier work this paper cites.
Weak convergence
Aad Van Der Vaart and Jon A Wellner · 1996
Earlier work this paper cites.
New donsker classes
Aad Van Der Vaart et al · 1996
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt · 1999
Earlier work this paper cites.
Asymptotic statistics , volume 3
Aad W Van der Vaart · 2000
Earlier work this paper cites.
Quantile regression
Roger Koenker and Kevin F Hallock · 2001
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
Earlier work this paper cites.
Confidence intervals and families of tests
Erich L Lehmann and Joseph P Romano · 2006
Earlier work this paper cites.
Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery · 2007
Earlier work this paper cites.
A statistical model of criminal behavior
Martin B Short, Maria R D’orsogna, Virginia B Pasour, George E Tita, Paul J Brantingham, Andrea L Bertozzi, and Lincoln B Chayes · 2008
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael Irwin Jordan · 2008
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
A simple normal approximation for weibull distribution with application to estimation of upper prediction limit
HV Kulkarni and SK Powar · 2011
Earlier work this paper cites.
Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness
James Mitchell and Kenneth F Wallis · 2011
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
Cited alongside, same era.
Gaussian processes for big data
James Hensman, Nicolò Fusi, and Neil D Lawrence · 2013
Cited alongside, same era.
Geographic health information systems: a platform to support the ‘triple aim’
Marie Lynn Miranda, Jeffrey Ferranti, Benjamin Strauss, Brian Neelon, and Robert M Califf · 2013
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
A critical review of statistical calibration/prediction models handling data inconsistency and model inadequacy
Pascal Pernot and Fabien Cailliez · 2017
Later among the works it cites.
Five things you should know about quantile regression
Robert N Rodriguez and Yonggang Yao · 2017
Later among the works it cites.
Doubly stochastic variational inference for deep gaussian processes
Hugh Salimbeni and Marc Deisenroth · 2017
Later among the works it cites.
Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu · 2018
Later among the works it cites.
Generative adversarial networks: An overview
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A Bharath · 2018
Later among the works it cites.
Gradient descent provably optimizes over-parameterized neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Modelling survival data in medical research
David Collett · 2015
Cited alongside, same era.
Batch normalization: accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (elus)
Clevert Djork-Arné, Thomas Unterthiner, and Sepp Hochreiter · 2016
Cited alongside, same era.
Uncertainty in Deep Learning
Yarin. Gal · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Preconditioned stochastic gradient langevin dynamics for deep neural networks
Chunyuan Li, Changyou Chen, David Carlson, and Lawrence Carin · 2016
Cited alongside, same era.
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
Later among the works it cites.
Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
Later among the works it cites.
High-quality prediction intervals for deep learning: A distribution-free, ensembled approach
Tim Pearce, Alexandra Brintrup, Mohamed Zaki, and Andy Neely · 2018
Later among the works it cites.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Later among the works it cites.
Modelling heterogeneous distributions with an uncountable mixture of asymmetric laplacians
Axel Brando, Jose A Rodriguez, Jordi Vitria, and Alberto Rubio Muñoz · 2019
Later among the works it cites.
Model misspecification in abc: consequences and diagnostics
David T Frazier, Christian Robert, and Judith Rousseau · 2019
Later among the works it cites.
Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel J Candès · 2019
Later among the works it cites.
Reliable training and estimation of variance networks
Nicki Skafte, Martin Jørgensen, and Søren Hauberg · 2019
Later among the works it cites.
Single-model uncertainties for deep learning
Natasa Tagasovska and David Lopez-Paz · 2019
Later among the works it cites.
On mixup training: improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
Later among the works it cites.
Parametric gaussian process regressors
Martin Jankowiak, Geoff Pleiss, and Jacob Gardner · 2020
Closest in time.
A universal approximation theorem of deep neural networks for expressing probability distributions, 2020
Yulong Lu and Jianfeng Lu · 2020
Closest in time.
Building calibrated deep models via uncertainty matching with auxiliary interval predictors
Jayaraman J Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, and Peer-Timo Bremer · 2020
Closest in time.
Quantile regularization: towards implicit calibration of regression models
Saiteja Utpala and Piyush Rai · 2020
Closest in time.