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Parvin Nazari

2 accepted papers

2025

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

NeurIPS 2025poster

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 syste…

Cited by 0SourceScholar
2024

Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods

AISTATS 2024poster

This paper introduces \textit{online bilevel optimization} in which a sequence of time-varying bilevel problems is revealed one after the other. We extend the known regret bounds for single-level online algorithms to the bilevel setting. Specifically, we provide new notions of \textit{bilevel regret…