Papers › Debiasing a First-order Heuristic for Approximate Bi-level Optimization

Debiasing a First-order Heuristic for Approximate Bi-level Optimization

4 Jun 2021arXiv:2106.02487archive 2025-07-28

Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, Adrian Weller

Approximate bi-level optimization (ABLO) consists of (outer-level) optimization problems, involving numerical (inner-level) optimization loops. While ABLO has many applications across deep learning, it suffers from time and memory complexity proportional to the length r of its inner optimization loop. To address this complexity, an earlier first-order method (FOM) was proposed as a heuristic that omits second derivative terms, yielding significant speed gains and requiring only constant memory. Despite FOM's popularity, there is a lack of theoretical understanding of its convergence properties. We contribute by theoretically characterizing FOM's gradient bias under mild assumptions. We further demonstrate a rich family of examples where FOM-based SGD does not converge to a stationary point of the ABLO objective. We address this concern by proposing an unbiased FOM (UFOM) enjoying constant memory complexity as a function of r. We characterize the introduced time-variance tradeoff, demonstrate convergence bounds, and find an optimal UFOM for a given ABLO problem. Finally, we propose an efficient adaptive UFOM scheme.

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get_div_problem xingyousong/ufom/toy/toy.py official repository ran · honoured contract fingerprinted MIT (permissive) · 195099a9682124b1 · report
get_true_blo_derivs xingyousong/ufom/toy/toy.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 4aba9cec39894458 · report
get_true_blo_values xingyousong/ufom/toy/toy.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 80f61075f0238ea9 · report

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