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Chongli Qin

A framework for robustness certification of smoothed classifiers using f-divergences
Achieving robustness in the wild via adversarial mixing with disentangled representations
Efficient neural network verification with exactness characterization
Adversarial robustness through local linearization
Scalable verified training for provably robust image classification
Verification of non-linear specifications for neural networks

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