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Algorithmic stability is a central notion in learning theory that quantifies the sensitivity of an algorithm to small changes in the training data.
A finite sample distribution-free performance bound for local discrimination rules
Rogers, W. H. and Wagner, T. J. (1978) · 1978
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Distribution-free inequalities for the deleted and holdout error estimates
Devroye, L. and Wagner, T. (1979) · 1979
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Interpolation of operators
Bennett, C. and Sharpley, R. C. (1988) · 1988
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An introduction to the bootstrap
Efron, B. and Tibshirani, R. J. (1994) · 1994
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Bagging predictors
Breiman, L. (1996) · 1996
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Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
Kearns, M. and Ron, D. (1997) · 1997
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Asymptotics of cross-validation
Austern, M. and Zhou, W. (2020) · 2001
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Stability and generalization
Bousquet, O. and Elisseeff, A. (2002) · 2002
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General conditions for predictivity in learning theory
Poggio, T., Rifkin, R., Mukherjee, S., and Niyogi, P. (2004) · 2004
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Stability of randomized learning algorithms
Elisseeff, A., Evgeniou, T., Pontil, M., and Kaelbing, L. P. (2005) · 2005
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Stability selection
Meinshausen, N. and Bühlmann, P. (2010) · 2010
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Learnability, stability and uniform convergence
Shalev-Shwartz, S., Shamir, O., Srebro, N., and Sridharan, K. (2010) · 2010
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Concrete functional calculus
Dudley, R. M., Norvaiša, R., and Norvaiša, R. (2011) · 2011
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Variable selection with error control: another look at stability selection
Shah, R. D. and Samworth, R. J. (2013) · 2013
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Stability
Yu, B. (2013) · 2013
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Train faster, generalize better: Stability of stochastic gradient descent
Hardt, M., Recht, B., and Singer, Y. (2016) · 2016
Cited alongside, same era.
Toward better generalization bounds with locally elastic stability
Deng, Z., He, H., and Su, W. (2021) · 2021
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Debiased machine learning without sample-splitting for stable estimators
Chen, Q., Syrgkanis, V., and Austern, M. (2022) · 2022
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Black-box model confidence sets using cross-validation with high-dimensional gaussian comparison
Kissel, N. and Lei, J. (2022) · 2022
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Black-box tests for algorithmic stability
Kim, B. and Barber, R. F. (2023) · 2023
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Algorithmic stability implies training-conditional coverage for distribution-free prediction methods
Liang, R. and Barber, R. F. (2023) · 2023
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Virmaux, A. and Scaman, K. (2018) · 2018
Cited alongside, same era.
Cross-validation confidence intervals for test error
Bayle, P., Bayle, A., Janson, L., and Mackey, L. (2020) · 2020
Cited alongside, same era.
Fine-grained analysis of stability and generalization for stochastic gradient descent
Lei, Y. and Ying, Y. (2020) · 2020
Cited alongside, same era.
Predictive inference with the jackknife+
Barber, R. F., Candès, E. J., Ramdas, A., and Tibshirani, R. J. (2021) · 2021
Cited alongside, same era.
Ren, Z., Wei, Y., and Candès, E. (2023) · 2023
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Conditional predictive inference for high-dimensional stable algorithms
Steinberger, L. and Leeb, H. (2023) · 2023
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Post-selection inference via algorithmic stability
Zrnic, T. and Jordan, M. I. (2023) · 2023
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Bagging provides assumption-free stability
Soloff, J. A., Barber, R. F., and Willett, R. (2024) · 2024
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