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Prospects and Perils of Interpolating Models

[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022

March 09, 2022 (08:30 AM PST - 08:55 AM PST)
Speaker(s): Fanny Yang (ETH Zurich)
Location: MSRI: Online/Virtual
  • interpolation

  • high-dimensional statistics

  • adversarial robustness

  • classification

  • regression

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Prospects And Perils Of Interpolating Models


In this talk, I will discuss several recent works from our group studying interpolating high-dimensional linear models. On the bright side, we show that for sparse ground truths, minimum-norm interpolators (including max-margin classifiers) can achieve high-dimensional asymptotic consistency and fast rates for isotropic Gaussian covariates. However, we also prove some caveats of such interpolating solutions in the context of robustness that are also observed for neural network learning: when performing adversarial training, interpolation can hurt robust test accuracy as compared to regularized solutions. Further, in the low-sample regime, the adversarially robust max-margin solution surprisingly can achieve lower robust accuracy than the standard max-margin classifier.

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Prospects And Perils Of Interpolating Models

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