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The Minimax Cost of Smoothness in Fully First-Order Stochastic Bilevel Optimization
We study the sample complexity of finding a point with expected hypergradient norm at most $ε>0$ in nonconvex--strongly-convex stochastic bilevel optimization using fresh, globally unbiased first-order samples with bounded variance. For lower-variable smoothness order $p\ge1$, F$^2$SA-$p$ achieves $
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