Online kernel regression equals offline regression with shifted targets; correcting the targets lets online learning match offline performance and outperform true targets in continual image classification.
Non-stationary stochastic optimization.Operations research, 63(5):1227–1244
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Algorithms achieve optimal regret bounds of Ω(1+V_T) for standard bilevel local regret with O(T log T) inner gradients and Ω(T/W²) for window-averaged regret using adaptive and window-based analyses.
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Characterizing and Correcting Effective Target Shift in Online Learning
Online kernel regression equals offline regression with shifted targets; correcting the targets lets online learning match offline performance and outperform true targets in continual image classification.
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Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization
Algorithms achieve optimal regret bounds of Ω(1+V_T) for standard bilevel local regret with O(T log T) inner gradients and Ω(T/W²) for window-averaged regret using adaptive and window-based analyses.