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Research questionHow can functional bilevel optimization adapt online as learning objectives change over time?Functional bilevel optimization is designed mainly for static, offline problems, where the objectives and data distribution do not change during optimization. Online learning requires its hierarchical updates to remain stable while responding to evolving objectives.
AI
Machine Learning
Reinforcement Learning
Research Paper
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Non-Stationary Functional Bilevel OptimizationThe source addresses non-stationary functional bilevel optimization and presents SmoothFBO, which uses a time-smoothed stochastic hypergradient estimator with a window parameter. It reports theoretical guarantees, sublinear regret, practical scalability, and experiments in non-stationary hyperparameter optimization and model-based reinforcement learning.research paper · Sep 2, 2026
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