Introduces a refereeing procedure and full computational cost accounting to improve benchmarking fairness for bilevel derivative-free optimization algorithms.
arXiv preprint arXiv:2412.18518 , year=
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W-SparQ-BL models time-varying lower-level responses with multi-output GPs and sparse approximations to achieve sublinear dynamic regret in bilevel optimization under noise.
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Benchmarking Bilevel Derivative-Free Optimization Algorithms
Introduces a refereeing procedure and full computational cost accounting to improve benchmarking fairness for bilevel derivative-free optimization algorithms.
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No-regret optimization of time-varying bilevel problems
W-SparQ-BL models time-varying lower-level responses with multi-output GPs and sparse approximations to achieve sublinear dynamic regret in bilevel optimization under noise.