arXiv:2207.02829
공식 코드 · Papers with Code
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods
원문 초록
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 online single-level algorithms to the bilevel setting. Specifically, we provide new notions of \textit{bilevel regret}, develop an online alternating time-averaged gradient method that is capable of leveraging smoothness, and give regret bounds in terms of the path-length of the inner and outer minimizer sequences.
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Learning Where to Learn: Training Distribution Selection for Provable OOD Performance
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HBO: Hierarchical Balancing Optimization for Fine-Tuning Large Language Models
Optimizing Decomposition for Optimal Claim Verification
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