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Bagging is an important technique for stabilizing machine learning models.
Iterative random forests to discover predictive and stable high-order interactions
Basu, S., Kumbier, K., Brown, J. B., and Yu, B. (2018) · 1948
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A finite sample distribution-free performance bound for local discrimination rules
Rogers, W. H. and Wagner, T. J. (1978) · 1978
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Bootstrap methods: another look at the jackknife
Efron, B. (1979) · 1979
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Algorithmic stability and sanity-check bounds for leave-one-out cross vaildation
Kearns, M. and Ron, D. (1999) · 1999
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Smoothing effects of bagging
Buja, A. and Stuetzle, W. (2000) · 2000
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Ensemble methods in machine learning
Dietterich, T. G. (2000) · 2000
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Random forests
Breiman, L. (2001) · 2001
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The interaction of stability and weakness in AdaBoost
Kutin, S. and Niyogi, P. (2001) · 2001
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Online bagging and boosting
Oza, N. C. and Russell, S. J. (2001) · 2001
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A simple algorithm for learning stable machines
Andonova, S., Elisseeff, A., Evgeniou, T., and Pontil, M. (2002) · 2002
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Stability and generalization
Bousquet, O. and Elisseeff, A. (2002) · 2002
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Analyzing bagging
Bühlmann, P. and Yu, B. (2002) · 2002
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Almost-everywhere algorithmic stability and generalization error
Kutin, S. and Niyogi, P. (2002) · 2002
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Bagging regularizes
Poggio, T., Rifkin, R., Mukherjee, S., and Rakhlin, A. (2002) · 2002
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Ensembles of learning machines
Valentini, G. and Masulli, F. (2002) · 2002
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Online ranking/collaborative filtering using the perceptron algorithm
Harrington, E. F. (2003) · 2003
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Generalization bounds for averaged classifiers
Freund, Y., Mansour, Y., and Schapire, R. E. (2004) · 2004
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Bagging equalizes influence
Grandvalet, Y. (2004) · 2004
Cited alongside, same era.
General conditions for predictivity in learning theory
Poggio, T., Rifkin, R., Mukherjee, S., and Niyogi, P. (2004) · 2004
Cited alongside, same era.
Stability of randomized learning algorithms
Elisseeff, A., Evgeniou, T., and Pontil, M. (2005) · 2005
Cited alongside, same era.
Stability of bagged decision trees
Grandvalet, Y. (2006) · 2006
Cited alongside, same era.
Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization
Mukherjee, S., Niyogi, P., Poggio, T., and Rifkin, R. (2006) · 2006
Cited alongside, same era.
Knowing when you’re wrong: building fast and reliable approximate query processing systems
Agarwal, S., Milner, H., Kleiner, A., Talwalkar, A., Jordan, M., Madden, S., Mozafari, B., and Stoica, I. (2014) · 2014
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A scalable bootstrap for massive data
Kleiner, A., Talwalkar, A., Sarkar, P., and Jordan, M. I. (2014) · 2014
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Train faster, generalize better: Stability of stochastic gradient descent
Hardt, M., Recht, B., and Singer, Y. (2016) · 2016
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Leave-one-out prediction intervals in linear regression models with many variables
Steinberger, L. and Leeb, H. (2016) · 2016
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Privacy-preserving prediction
Dwork, C. and Feldman, V. (2018) · 2018
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Stability of k k -means clustering
Ben-David, S., Pál, D., and Simon, H. U. (2007) · 2007
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On bagging and nonlinear estimation
Friedman, J. H. and Hall, P. (2007) · 2007
Cited alongside, same era.
Differential privacy: a survey of results
Dwork, C. (2008) · 2008
Cited alongside, same era.
Sufficient conditions for uniform stability of regularization algorithms
Wibisono, A., Rosasco, L., and Poggio, T. (2009) · 2009
Cited alongside, same era.
Stability selection
Meinshausen, N. and Bühlmann, P. (2010) · 2010
Cited alongside, same era.
Learnability, stability and uniform convergence
Shalev-Shwartz, S., Shamir, O., Srebro, N., and Sridharan, K. (2010) · 2010
Cited alongside, same era.
Candès, E. J. and Sur, P. (2020) · 2020
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The implicit regularization of ordinary least squares ensembles
LeJeune, D., Javadi, H., and Baraniuk, R. (2020) · 2020
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Predictive inference with the jackknife+
Barber, R. F., Candès, E. J., Ramdas, A., and Tibshirani, R. J. (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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Stable conformal prediction sets
Ndiaye, E. (2022) · 2022
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Black-box tests for algorithmic stability
Kim, B. and Barber, R. F. (2023) · 2023
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Bagging is an optimal PAC learner
Larsen, K. G. (2023) · 2023
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Bagging in overparameterized learning: Risk characterization and risk monotonization
Patil, P., Du, J.-H., and Kuchibhotla, A. K. (2023) · 2023
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Derandomizing knockoffs
Ren, Z., Wei, Y., and Candès, E. (2023) · 2023
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Conditional predictive inference for stable algorithms
Steinberger, L. and Leeb, H. (2023) · 2023
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Asymptotics of the Sketched Pseudoinverse
LeJeune, D., Patil, P., Javadi, H., Baraniuk, R. G., and Tibshirani, R. J. (2024) · 2024
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