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.
arXiv preprint arXiv:1612.00547 , year=
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Introduces the SANC algorithm combining negative curvature with stochastic adaptive cubic regularization for nonconvex optimization and claims it is the first such combination with consistent batch sizes for large-scale ML.
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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.
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Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization
Introduces the SANC algorithm combining negative curvature with stochastic adaptive cubic regularization for nonconvex optimization and claims it is the first such combination with consistent batch sizes for large-scale ML.