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Machine learning applications often require calibrated predictions, e.g.
Verification of forecasts expressed in terms of probability
Brier, G. W · 1950
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A new vector partition of the probability score
Murphy, A. H · 1973
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Two-piece von neumann-morgenstern utility functions
Fishburn, P. C. and Kochenberger, G. A · 1979
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Present position and potential developments: Some personal views statistical theory the prequential approach
Dawid, A. P · 1984
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Classical and modern regression with applications , volume 2
Myers, R. H. and Myers, R. H · 1990
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J. et al · 1999
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Zadrozny, B. and Elkan, C · 2001
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Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B. and Elkan, C · 2002
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Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
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Algorithmic learning in a random world
Vovk, V., Gammerman, A., and Shafer, G · 2005
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Prediction, learning, and games
Cesa-Bianchi, N. and Lugosi, G · 2006
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Probabilistic forecasts, calibration and sharpness
Gneiting, T., Balabdaoui, F., and Raftery, A. E · 2007
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Urban security: Game-theoretic resource allocation in networked domains
Tsai, J., Yin, Z., Kwak, J.-y., Kempe, D., Kiekintveld, C., and Tambe, M · 2010
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Online learning and online convex optimization
Shalev-Shwartz, S. et al · 2012
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Conditional validity of inductive conformal predictors
Vovk, V · 2012
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C · 2013
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The variational fair autoencoder
Louizos, C., Swersky, K., Li, Y., Welling, M., and Zemel, R · 2015
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Kearns, M., Neel, S., Roth, A., and Wu, Z. S · 2017
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Avoiding discrimination through causal reasoning
Kilbertus, N., Carulla, M. R., Parascandolo, G., Hardt, M., Janzing, D., and Schölkopf, B · 2017
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Counterfactual fairness
Kusner, M. J., Loftus, J., Russell, C., and Silva, R · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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On fairness and calibration
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., and Weinberger, K. Q · 2017
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Accurate uncertainties for deep learning using calibrated regression
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Trejo, K. K., Clempner, J. B., and Poznyak, A. S · 2015
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Friedler, S. A., Scheidegger, C., and Venkatasubramanian, S · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., Srebro, N., et al · 2016
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Fairness in learning: Classic and contextual bandits
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Inherent trade-offs in the fair determination of risk scores
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Algorithmic decision making and the cost of fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., and Huq, A · 2017
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Kuleshov, V., Fenner, N., and Ermon, S · 2018
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The implicit fairness criterion of unconstrained learning
Liu, L. T., Simchowitz, M., and Hardt, M · 2018
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Learning controllable fair representations
Song, J., Kalluri, P., Grover, A., Zhao, S., and Ermon, S · 2018
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The limits of distribution-free conditional predictive inference
Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J · 2019
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Average individual fairness: Algorithms, generalization and experiments
Kearns, M., Roth, A., and Sharifi-Malvajerdi, S · 2019
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Evaluating and calibrating uncertainty prediction in regression tasks
Levi, D., Gispan, L., Giladi, N., and Fetaya, E · 2019
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Calibrated model-based deep reinforcement learning
Malik, A., Kuleshov, V., Song, J., Nemer, D., Seymour, H., and Ermon, S · 2019
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Creating fair models of atherosclerotic cardiovascular disease risk
Pfohl, S., Marafino, B., Coulet, A., Rodriguez, F., Palaniappan, L., and Shah, N. H · 2019
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Perdomo, J. C., Zrnic, T., Mendler-Dünner, C., and Hardt, M · 2020
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