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- W3206661898 abstract "We study the Extended Kalman Filter in constant dynamics, offering a bayesian perspective of stochastic optimization. We obtain high probability bounds on the cumulative excess risk in an unconstrained setting. In order to avoid any projection step we propose a two-phase analysis. First, for linear and logistic regressions, we prove that the algorithm enters a local phase where the estimate stays in a small region around the optimum. We provide explicit bounds with high probability on this convergence time. Second, for generalized linear regressions, we provide a martingale analysis of the excess risk in the local phase, improving existing ones in bounded stochastic optimization. The EKF appears as a parameter-free online algorithm with O(d^2) cost per iteration that optimally solves some unconstrained optimization problems." @default.
- W3206661898 created "2021-10-25" @default.
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- W3206661898 date "2020-06-23" @default.
- W3206661898 modified "2023-09-27" @default.
- W3206661898 title "Stochastic Online Optimization using Kalman Recursion" @default.
- W3206661898 hasPublicationYear "2020" @default.
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