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• W2952550984 abstract "We study the generalization performance of online learning algorithms trained on samples coming from a dependent source of data. We show that the generalization error of any stable online algorithm concentrates around its regret--an easily computable statistic of the online performance of the algorithm--when the underlying ergodic process is $beta$- or $phi$-mixing. We show high probability error bounds assuming the loss function is convex, and we also establish sharp convergence rates and deviation bounds for strongly convex losses and several linear prediction problems such as linear and logistic regression, least-squares SVM, and boosting on dependent data. In addition, our results have straightforward applications to stochastic optimization with dependent data, and our analysis requires only martingale convergence arguments; we need not rely on more powerful statistical tools such as empirical process theory." @default.
• W2952550984 created "2019-06-27" @default.
• W2952550984 creator A5036435487 @default.
• W2952550984 creator A5061331093 @default.
• W2952550984 date "2011-10-11" @default.
• W2952550984 modified "2023-09-27" @default.
• W2952550984 title "The Generalization Ability of Online Algorithms for Dependent Data" @default.
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