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- W2915693723 abstract "We consider online optimization procedures in the context of logistic regression, focusing on the Extended Kalman Filter (EKF). We introduce a second-order algorithm close to the EKF, named Semi-Online Step (SOS), for which we prove a O(log(n)) regret in the adversarial setting, paving the way to similar results for the EKF. This regret bound on SOS is the first for such parameter-free algorithm in the adversarial logistic regression. We prove for the EKF in constant dynamics a O(log(n)) regret in expectation and in the well-specified logistic regression model." @default.
- W2915693723 created "2019-03-02" @default.
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- W2915693723 date "2019-02-22" @default.
- W2915693723 modified "2023-09-25" @default.
- W2915693723 title "Logarithmic Regret for parameter-free Online Logistic Regression" @default.
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