Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization

arXiv:2511.01126v3 Announce Type: replace Abstract: Online bilevel optimization (OBO) is a powerful framework for machine learning problems where both outer and inner objectives evolve over time, requiring dynamic updates. Current OBO approaches rely on deterministic \textit{window-smoothed} regret minimization, which may not accurately reflect system performance when functions change rapidly. In this work, we introduce a novel search direction and show that both first- and zeroth-order (ZO) stochastic OBO algorithms leveraging this direction achieve sublinear {stochastic bilevel regret withou

Source: arXiv cs.LG — read the full report at the original publisher.

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