A deterministic single-loop cubic regularized Newton method for NCSC bilevel optimization that attains the optimal O(ε^{-1.5}) SOSP rate without repeated lower-level solves.
arXiv preprint arXiv:2502.09074 , year=
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Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.
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On Second-Order Methods for Bilevel Optimization
A deterministic single-loop cubic regularized Newton method for NCSC bilevel optimization that attains the optimal O(ε^{-1.5}) SOSP rate without repeated lower-level solves.
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Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.