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It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the robust t...
We report 3 recent works along this direction: (A) The nature of L1, L21 error function robustness, using an outlier regularization framework.(B)Labeled robust principal component analysis (RPCA), by ...
This report consists of two parts associated with graph neural networks: generalization and graph structural learning. We first study the Rademacher complexity of GNNs, as one of independent-algorithm...

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