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.
Autobalance: Optimized loss functions for imbalanced data.CoRR, abs/2201.01212
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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.