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The prediction accuracy of machine learning methods is steadily increasing, but the calibration of their uncertainty predictions poses a significant challenge.
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A subjectivist view of calibration
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Probabilistic prediction in patient management and clinical trials
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Minimax testing of nonparametric hypotheses on a distribution density in the
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Minimax quadratic estimation of a quadratic functional
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On a problem of adaptive estimation in gaussian white noise
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Estimating nonquadratic functionals of a density using haar wavelets
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Efficient estimation of integral functionals of a density
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Adaptive hypothesis testing using wavelets
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Grobner Bases and Convex Polytopes
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Scoring rules and the evaluation of probabilities
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Optimal pointwise adaptive methods in nonparametric estimation
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On nonparametric confidence intervals
M. G. Low · 1997
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Evaluating density forecasts with applications to financial risk management
F. X. Diebold, T. A. Gunther, and A. S. Tay · 1998
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Asymptotic calibration
D. P. Foster and R. V. Vohra · 1998
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Classification by pairwise coupling
T. Hastie and R. Tibshirani · 1998
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Calibration
A. Franklin · 1999
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Ready-made, recalibrated, or remodeled? issues in the use of risk indexes for assessing mortality after coronary artery bypass graft surgery
J. Ivanov, J. V. Tu, and C. D. Naylor · 1999
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
J. Platt · 1999
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Rare and weak effects in large-scale inference: methods and phase diagrams
J. Jin and Z. T. Ke · 2016
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Superforecasting: The Art and Science of Prediction
P. E. Tetlock and D. Gardner · 2016
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A calibration hierarchy for risk models was defined: from utopia to empirical data
B. Van Calster, D. Nieboer, Y. Vergouwe, B. De Cock, M. J. Pencina, and E. W. Steyerberg · 2016
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An impact assessment of machine learning risk forecasts on parole board decisions and recidivism
R. Berk · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
A. Esteva, B. Kuprel, R. A. Novoa, J. M. Ko, S. M. Swetter, H. M. Blau, and S. Thrun · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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Adaptive chi-square tests
Y. I. Ingster · 2000
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Calibration
C. Dawkins, T. N. Srinivasan, and J. Whalley · 2001
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
B. Zadrozny and C. P. Elkan · 2001
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Classification rules in standardized partition spaces
U. Garczarek · 2002
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A distribution-free theory of nonparametric regression
L. Györfi, M. Kohler, A. Krzyżak, and H. Walk · 2002
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Transforming classifier scores into accurate multiclass probability estimates
B. Zadrozny and C. P. Elkan · 2002
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Beyond sigmoids: how to obtain well-calibrated probabilities from binary classifiers with beta calibration
M. Kull, T. M. Silva Filho, and P. Flach · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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Remember the curse of dimensionality: the case of goodness-of-fit testing in arbitrary dimension
E. Arias-Castro, B. Pelletier, and V. Saligrama · 2018
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Hypothesis testing for high-dimensional multinomials: a selective review
S. Balakrishnan and L. Wasserman · 2018
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Trainable calibration measures for neural networks from kernel mean embeddings
A. Kumar, S. Sarawagi, and U. Jain · 2018
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Dirichlet-based gaussian processes for large-scale calibrated classification
D. Milios, R. Camoriano, P. Michiardi, L. Rosasco, and M. Filippone · 2018
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Cross-fitting and fast remainder rates for semiparametric estimation
W. K. Newey and J. R. Robins · 2018
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Big data and predictive analytics: recalibrating expectations
N. D. Shah, E. W. Steyerberg, and D. M. Kent · 2018
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High-dimensional probability: An introduction with applications in data science
R. Vershynin · 2018
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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
A. Kumar, P. Liang, and T. Ma · 2019
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Measuring calibration in deep learning
J. Nixon, M. W. Dusenberry, L. Zhang, G. Jerfel, and D. Tran · 2019
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Clinical prediction models
E. W. Steyerberg · 2019
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On mixup training: improved calibration and predictive uncertainty for deep neural networks
S. Thulasidasan, G. Chennupati, J. A. Bilmes, T. Bhattacharya, and S. E. Michalak · 2019
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Evaluating model calibration in classification
J. Vaicenavicius, D. Widmann, C. Andersson, F. Lindsten, J. Roll, and T. Schön · 2019
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Calibration: the achilles heel of predictive analytics
B. Van Calster, D. J. McLernon, M. Van Smeden, L. Wynants, and E. W. Steyerberg · 2019
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Calibration tests in multi-class classification: a unifying framework
D. Widmann, F. Lindsten, and D. Zachariah · 2019
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Distribution-free binary classification: prediction sets, confidence intervals and calibration
C. Gupta, A. Podkopaev, and A. Ramdas · 2020
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A tutorial on calibration measurements and calibration models for clinical prediction models
Y. Huang, W. Li, F. Macheret, R. A. Gabriel, and L. Ohno-Machado · 2020
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Optimal doubly robust estimation of heterogeneous causal effects
E. H. Kennedy · 2020
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Simulator calibration under covariate shift with kernels
K. Kisamori, M. Kanagawa, and K. Yamazaki · 2020
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Calibrating deep neural networks using focal loss
J. Mukhoti, V. Kulharia, A. Sanyal, S. Golodetz, P. Torr, and P. Dokania · 2020
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Optimal estimation of variance in nonparametric regression with random design
Y. Shen, C. Gao, D. Witten, and F. Han · 2020
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Conformal calibrators
V. Vovk, I. Petej, P. Toccaceli, A. Gammerman, E. Ahlberg, and L. Carlsson · 2020
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Mix-n-match: ensemble and compositional methods for uncertainty calibration in deep learning
J. Zhang, B. Kailkhura, and T. Y.-J. Han · 2020
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Individual calibration with randomized forecasting
S. Zhao, T. Ma, and S. Ermon · 2020
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Learn then test: Calibrating predictive algorithms to achieve risk control
A. N. Angelopoulos, S. Bates, E. J. Candès, M. I. Jordan, and L. Lei · 2021
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Don’t just blame over-parametrization for over-confidence: Theoretical analysis of calibration in binary classification
Y. Bai, S. Mei, H. Wang, and C. Xiong · 2021
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Optimal rates for independence testing via u-statistic permutation tests
T. B. Berrett, I. Kontoyiannis, and R. J. Samworth · 2021
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Pytorch cifar models, 2021
Y. Chen · 2021
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Goodness-of-fit testing for Hölder-continuous densities: Sharp local minimax rates
J. Chhor and A. Carpentier · 2021
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Goodness-of-fit testing for Hölder continuous densities under local differential privacy
A. Dubois, T. Berrett, and C. Butucea · 2021
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Mathematical foundations of infinite-dimensional statistical models
E. Giné and R. Nickl · 2021
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Calibration of neural networks using splines
K. Gupta, A. Rahimi, T. Ajanthan, T. Mensink, C. Sminchisescu, and R. Hartley · 2021
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Exact distribution-free hypothesis tests for the regression function of binary classification via conditional kernel mean embeddings
A. Tamás and B. C. Csáji · 2021
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Metrics of calibration for probabilistic predictions
I. Arrieta-Ibarra, P. Gujral, J. Tannen, M. Tygert, and C. Xu · 2022
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Top-label calibration and multiclass-to-binary reductions
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Minimax optimality of permutation tests
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Mitigating bias in calibration error estimation
R. Roelofs, N. Cain, J. Shlens, and M. C. Mozer · 2022
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