Feb. 9, 2024, 5:44 a.m. | Lesi Chen Jing Xu Jingzhao Zhang

cs.LG updates on arXiv.org arxiv.org

Bilevel optimization reveals the inner structure of otherwise oblique optimization problems, such as hyperparameter tuning, neural architecture search, and meta-learning. A common goal in bilevel optimization is to minimize a hyper-objective that implicitly depends on the solution set of the lower-level function. Although this hyper-objective approach is widely used, its theoretical properties have not been thoroughly investigated in cases where \textit{the lower-level functions lack strong convexity}. In this work, we first provide hardness results to show that the goal of …

analysis architecture cs.ai cs.lg function hyperparameter math.oc meta meta-learning neural architecture search optimization search set small solution

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