A zeroth-order proximal-gradient method with Gaussian smoothing is proved to find approximate stationary points of stochastic bilevel inverse problems with non-smooth convex lower levels, at oracle complexity O(epsilon^-3) in the nonconvex case and with explicit accuracy in the convex case.
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Derivative-free stochastic bilevel optimization for inverse problems
A zeroth-order proximal-gradient method with Gaussian smoothing is proved to find approximate stationary points of stochastic bilevel inverse problems with non-smooth convex lower levels, at oracle complexity O(epsilon^-3) in the nonconvex case and with explicit accuracy in the convex case.