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Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging.
Conformal prediction under covariate shift
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Koh, P. W. and Liang, P · 2017
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Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Giordano, R., Stephenson, W., Liu, R., Jordan, M. I., and Broderick, T · 2018
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Wager, S., Hastie, T., and Efron, B · 2014
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Hernández-Lobato, J. M. and Adams, R · 2015
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Kingma, D. P., Salimans, T., and Welling, M · 2015
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LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Gal, Y · 2016
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Gal, Y. and Ghahramani, Z · 2016
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Malinin, A. and Gales, M · 2018
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Estimation and inference of heterogeneous treatment effects using random forests
Wager, S. and Athey, S · 2018
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Analyzing the role of model uncertainty for electronic health records
Dusenberry, M. W., Tran, D., Choi, E., Kemp, J., Nixon, J., Jerfel, G., Heller, K., and Dai, A. M · 2019
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A higher-order swiss army infinitesimal jackknife
Giordano, R., Jordan, M. I., and Broderick, T · 2019
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A simple baseline for bayesian uncertainty in deep learning
Maddox, W., Garipov, T., Izmailov, P., Vetrov, D., and Wilson, A. G · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J · 2019
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Can you trust this prediction? auditing pointwise reliability after learning
Schulam, P. and Saria, S · 2019
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