BROS achieves memory-efficient single-loop stochastic bilevel optimization with O(ε^{-2}) sample complexity by performing updates in randomized subspaces and using Rademacher bi-probe correction for unbiased estimation.
SPABA: A single-loop and probabilistic stochastic bilevel algorithm achieving optimal sample complexity
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S2MAM uses a probabilistic bilevel optimization scheme to learn binary masks on input variables, simultaneously performing variable selection and adaptive graph Laplacian construction for robust semi-supervised additive regression.
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BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization
BROS achieves memory-efficient single-loop stochastic bilevel optimization with O(ε^{-2}) sample complexity by performing updates in randomized subspaces and using Rademacher bi-probe correction for unbiased estimation.
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S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
S2MAM uses a probabilistic bilevel optimization scheme to learn binary masks on input variables, simultaneously performing variable selection and adaptive graph Laplacian construction for robust semi-supervised additive regression.