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Found 5 papers, 2 papers with code
Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization
All the proposed complexities indicate that our proposed methods can match lower bounds to existing minimax optimizations, without requiring a large batch size in each iteration.
Multi-source adversarial transfer learning based on similar source domains with local features
In order to ensure that the data from the target domain in different sub-networks in the same batch is exactly the same, we designed a multi-source domain independent strategy to provide the possibility for later local feature fusion to complete the key features required.
A Certified Radius-Guided Attack Framework to Image Segmentation Models
We are the first, from the attacker perspective, to leverage the properties of certified radius and propose a certified radius guided attack framework against image segmentation models.
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees
We then propose an online attack based on bandit optimization which is proven to be {sublinear} to the query number $T$, i. e., $\mathcal{O}(\sqrt{N}T^{3/4})$ where $N$ is the number of nodes in the graph.
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation
However, many recent works have demonstrated that an attacker can mislead GNN models by slightly perturbing the graph structure.
