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An important component in deploying machine learning (ML) in safety-critic applications is having a reliable measure of confidence in the ML model's predictions.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D. (2019) · 1907
Earlier work this paper cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019) · 1910
Earlier work this paper cites.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P. (2019) · 1911
Earlier work this paper cites.
Multicalibration: Calibration for the (computationally-identifiable) masses
Hébert-Johnson, U., Kim, M., Reingold, O., and Rothblum, G. (2018) · 1948
Earlier work this paper cites.
A new vector partition of the probability score
Murphy, A. H. (1973) · 1973
Earlier work this paper cites.
The well-calibrated bayesian
Dawid, A. P. (1982) · 1982
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J. et al. (1999) · 1999
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Zadrozny, B. and Elkan, C. (2001) · 2001
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B. and Elkan, C. (2002) · 2002
Earlier work this paper cites.
Calibration of pre-trained transformers
Desai, S. and Durrett, G. (2020) · 2003
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Probabilistic forecasts, calibration and sharpness
Gneiting, T., Balabdaoui, F., and Raftery, A. E. (2007) · 2007
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Active learning literature survey
Settles, B. (2009) · 2009
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Calibrated structured prediction
Kuleshov, V. and Liang, P. S. (2015) · 2015
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Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G., and Hauskrecht, M. (2015) · 2015
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017) · 2017
Cited alongside, same era.
Measuring calibration in deep learning
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Calibration tests in multi-class classification: A unifying framework
Widmann, D., Lindsten, F., and Zachariah, D. (2019) · 2019
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Addressing bias in prediction models by improving subpopulation calibration
Barda, N., Yona, G., Rothblum, G. N., Greenland, P., Leibowitz, M., Balicer, R., Bachmat, E., and Dagan, N. (2021) · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2021) · 2021
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Calibrating predictions to decisions: A novel approach to multi-class calibration
Zhao, S., Kim, M., Sahoo, R., Ma, T., and Ermon, S. (2021) · 2021
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
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