13:00
Certifying robustness via topological representations
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Abstract
In this talk we discuss how the stability results for the invariants of Topological Data Analysis can be exploited to design machine learning models with robustness guarantees. We propose a neural network architecture that can learn discriminative geometric representations of data from persistence diagrams. The learned representations enjoy Lipschitz stability with a controllable Lipschitz constant. In adversarial learning, this stability can be used to certify robustness for samples in a dataset, as we demonstrate on synthetic data.