MGS is a primal-dual stochastic gradient algorithm for constrained simulation optimization that converges to a KKT point at rate ilde{O}(T^{-1/3}).
arXiv preprint arXiv:2510.19165 , year=
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The paper demonstrates a black-box model extraction attack on graph classification models that leverages binary subgraph explanations to guide Monte Carlo edge sensitivity estimation with concentration guarantees.
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A Min-Max Gradient Search Method for Constrained Simulation Optimization
MGS is a primal-dual stochastic gradient algorithm for constrained simulation optimization that converges to a KKT point at rate ilde{O}(T^{-1/3}).
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Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
The paper demonstrates a black-box model extraction attack on graph classification models that leverages binary subgraph explanations to guide Monte Carlo edge sensitivity estimation with concentration guarantees.