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Fanny YANG (ETH Zurich)- ” How the strength of the inductive bias affects the generalization performance of interpolators “
Statistical Seminar: Every Monday at 2:00 pm.
Time: 2:00 pm – 3:15 pm
Date: 21th of November 2022
Place: salle 3001
Fanny YANG (ETH Zurich)- ” How the strength of the inductive bias affects the generalization performance of interpolators ”
Abstract:Interpolating models have recently gained popularity in the statistical learning community due to common practices in modern machine learning: complex models achieve good generalization performance despite interpolating high-dimensional training data. In this talk, we prove generalization bounds for high-dimensional linear models that interpolate noisy data generated by a sparse ground truth. In particular, we first show that minimum-l1-norm interpolators achieve high-dimensional asymptotic consistency at a logarithmic rate. Further, as opposed to the regularized or noiseless case, for min-lp-norm interpolators with 1<p<2 we surprisingly obtain polynomial rates. Our results suggest a new trade-off for interpolating models: a stronger inductive bias encourages a simpler structure better aligned with the ground truth at the cost of an increased variance. We finally discuss our latest results, where we show that this phenomenon also holds for nonlinear models.
Cristina BUTUCEA (CREST), Alexandre TSYBAKOV (CREST), Karim LOUNICI (CMAP) , Jaouad MOURTADA (CREST)