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A medley of geometry, optimal transport, and machine learning

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If you have a question about this talk, please contact Hamza Fawzi.

Modern machine learning algorithms are surprisingly fragile to adversarial perturbations of data. In this talk, we present some theoretical contributions towards understanding fundamental bounds on the performance of machine learning algorithms in the presence of adversaries. We shall discuss how optimal transport emerges as a natural mathematical tool to characterize “robust risk”, a notion of risk in the adversarial machine learning literature analogous to Bayes risk in hypothesis testing. We shall also show how, in addition to tools from optimal transport, we may use reverse-isoperimetric inequalities from geometry to provide theoretical bounds on the sample size of estimating robust risk.

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Meeting ID: 939 3334 2683 Passcode: U22EK PmS

This talk is part of the CCIMI Seminars series.

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