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Estimating the uncertainty of a model's prediction on a test point is a crucial part of ensuring reliability and calibration under distribution shifts.
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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., 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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Influence functions in deep learning are fragile
Basu, S., Pope, P., and Feizi, S · 2020
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Deep pnml: Predictive normalized maximum likelihood for deep neural networks, 2020
Bibas, K., Fogel, Y., and Feder, M · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation, 2020
Feldman, V. and Zhang, C · 2020
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Bojarski, M., Testa, D. D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., and Zieba, K · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Farnia, F. and Tse, D · 2017
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Does learning require memorization? a short tale about a long tail, 2021
Feldman, V · 2021
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Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation, 2021
Kim, T., Oh, J., Kim, N., Cho, S., and Yun, S.-Y · 2021
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Stronger data poisoning attacks break data sanitization defenses, 2021
Koh, P. W., Steinhardt, J., and Liang, P · 2021
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Deep learning on a data diet: Finding important examples early in training
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A simple fix to mahalanobis distance for improving near-ood detection, 2021
Ren, J., Fort, S., Liu, J., Roy, A. G., Padhy, S., and Lakshminarayanan, B · 2021
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Amortized conditional normalized maximum likelihood: Reliable out of distribution uncertainty estimation, 2021
Zhou, A. and Levine, S · 2021
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Clusterability as an alternative to anchor points when learning with noisy labels
Zhu, Z., Song, Y., and Liu, Y · 2021
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If influence functions are the answer, then what is the question?
Bae, J., Ng, N., Lo, A., Ghassemi, M., and Grosse, R. B · 2022
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Machine learning and health need better values
Ghassemi, M. and Mohamed, S · 2022
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Datamodels: Predicting predictions from training data, 2022
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Tanno, R., F. Pradier, M., Nori, A., and Li, Y · 2022
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Learning with noisy labels revisited: A study using real-world human annotations
Wei, J., Zhu, Z., Cheng, H., Liu, T., Niu, G., and Liu, Y · 2022
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Openood: Benchmarking generalized out-of-distribution detection
Yang, J., Wang, P., Zou, D., Zhou, Z., Ding, K., Peng, W., Wang, H., Chen, G., Li, B., Sun, Y., Du, X., Zhou, K., Zhang, W., Hendrycks, D., Li, Y., and Liu, Z · 2022
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Revisiting minimum description length complexity in overparameterized models
Dwivedi, R., Singh, C., Yu, B., and Wainwright, M · 2023
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Influence diagnostics under self-concordance
Fisher, J., Liu, L., Pillutla, K., Choi, Y., and Harchaoui, Z · 2023
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Studying large language model generalization with influence functions, 2023
Grosse, R., Bae, J., Anil, C., Elhage, N., Tamkin, A., Tajdini, A., Steiner, B., Li, D., Durmus, E., Perez, E., Hubinger, E., Lukošiūtė, K., Nguyen, K., Joseph, N., McCandlish, S., Kaplan, J., and Bowman, S. R · 2023
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Trak: Attributing model behavior at scale
Park, S. M., Georgiev, K., Ilyas, A., Leclerc, G., and Madry, A · 2023
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An empirical study of automated mislabel detection in real world vision datasets, 2023
Srikanth, M., Irvin, J., Hill, B. W., Godoy, F., Sabane, I., and Ng, A. Y · 2023
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Openood v1.5: Enhanced benchmark for out-of-distribution detection
Zhang, J., Yang, J., Wang, P., Wang, H., Lin, Y., Zhang, H., Sun, Y., Du, X., Zhou, K., Zhang, W., Li, Y., Liu, Z., Chen, Y., and Li, H · 2023
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