292 citations · 770 across the 46 of their papers we have counts for
4 papers · 1 filter
A novel statistical approach to analyze image classification
Juntong Chen, Sophie Langer, Johannes Schmidt-Hieber
The recent statistical theory of neural networks focuses on nonparametric denoising problems that treat randomness as additive noise. Variability in image classification datasets d…
On the inability of Gaussian process regression to optimally learn compositional functions
Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber
We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure. To this end, we study information…
Local convergence rates of the nonparametric least squares estimator with applications to transfer learning
Johannes Schmidt-Hieber, Petr Zamolodtchikov
Convergence properties of empirical risk minimizers can be conveniently expressed in terms of the associated population risk. To derive bounds for the performance of the estimator…
On generalization bounds for deep networks based on loss surface implicit regularization
Masaaki Imaizumi, Johannes Schmidt-Hieber
The classical statistical learning theory implies that fitting too many parameters leads to overfitting and poor performance. That modern deep neural networks generalize well despi…