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Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced.
Sharper bounds for uniformly stable algorithms
Bousquet, O., Klochkov, Y., and Zhivotovskiy, N. (2019) · 1910
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A stochastic approximation method
Robbins, H. and Monro, S. (1951) · 1951
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Stochastic estimation of the maximum of a regression function
Kiefer, J. and Wolfowitz, J. (1952) · 1952
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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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Problem Complexity and Method Efficiency in Optimization
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Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
Kearns, M. J. and Ron, D. (1999) · 1999
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Incremental subgradient methods for nondifferentiable optimization
Nedic, A. and Bertsekas, D. P. (2001) · 2001
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Stability and generalization
Bousquet, O. and Elisseeff, A. (2002) · 2002
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Online convex programming and generalized infinitesimal gradient ascent
Zinkevich, M. (2003) · 2003
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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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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
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Privacy-preserving logistic regression
Chaudhuri, K. and Monteleoni, C. (2008) · 2008
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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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Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D. (2011) · 2011
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Differentially private online learning
Jain, P., Kothari, P., and Thakurta, A. (2012) · 2012
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Private convex empirical risk minimization and high-dimensional regression
Kifer, D., Smith, A., and Thakurta, A. (2012) · 2012
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Differentially private feature selection via stability arguments, and the robustness of the LASSO
Smith, A. and Thakurta, A. (2013) · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Bassily, R., Smith, A., and Thakurta, A. (2014) · 2014
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(near) dimension independent risk bounds for differentially private learning
Jain, P. and Thakurta, A. (2014) · 2014
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Fast rates for exp-concave empirical risk minimization
Algorithmic stability and hypothesis complexity
Liu, T., Lugosi, G., Neu, G., and Tao, D. (2017) · 2017
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A pac-bayesian analysis of randomized learning with application to stochastic gradient descent
London, B. (2017) · 2017
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A second-order look at stability and generalization
Maurer, A. (2017) · 2017
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Bolt-on differential privacy for scalable stochastic gradient descent-based analytics
Wu, X., Li, F., Kumar, A., Chaudhuri, K., Jha, S., and Naughton, J. (2017) · 2017
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Stability and generalization of learning algorithms that converge to global optima
Charles, Z. and Papailiopoulos, D. (2018) · 2018
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Stability and convergence trade-off of iterative optimization algorithms
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Koren, T. and Levy, K. (2015) · 2015
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Lecture Notes. 18.S997: High Dimensional Statistics
Rigollet, P. (2015. https://ocw.mit.edu/courses/mathematics/18-s997-high-dimensional-statistics-spring-2015) · 2015
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Nearly optimal private LASSO
Talwar, K., Thakurta, A., and Zhang, L. (2015) · 2015
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Private multiplicative weights beyond linear queries
Ullman, J. (2015) · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L. (2016) · 2016
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Generalization of ERM in stochastic convex optimization: The dimension strikes back
Feldman, V. (2016) · 2016
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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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Chen, Y., Jin, C., and Yu, B. (2018) · 2018
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Dwork, C. and Feldman, V. (2018) · 2018
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Privacy amplification by iteration
Feldman, V., Mironov, I., Talwar, K., and Thakurta, A. (2018) · 2018
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Generalization bounds for uniformly stable algorithms
Feldman, V. and Vondrák, J. (2018) · 2018
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Data-dependent stability of stochastic gradient descent
Kuzborskij, I. and Lampert, C. H. (2018) · 2018
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Private stochastic convex optimization with optimal rates
Bassily, R., Feldman, V., Talwar, K., and Thakurta, A. G. (2019) · 2019
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High probability generalization bounds for uniformly stable algorithms with nearly optimal rate
Feldman, V. and Vondrák, J. (2019) · 2019
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Private stochastic convex optimization: Optimal rates in linear time
Feldman, V., Koren, T., and Talwar, K. (2020) · 2020
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