Introduces a novel search direction enabling sublinear stochastic bilevel regret guarantees for first- and zeroth-order online bilevel optimization algorithms without relying on window smoothing.
On the information-adaptive variants of the admm: an iteration complexity perspective.Journal of Scientific Computing, 76:327–363
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Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization
Introduces a novel search direction enabling sublinear stochastic bilevel regret guarantees for first- and zeroth-order online bilevel optimization algorithms without relying on window smoothing.