Konstantin Donhauser | DeepAI

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∙ 02/19/2023

Sample-efficient private data release for Lipschitz functions under sparsity assumptions

Differential privacy is the de facto standard for protecting privacy in ...

0 Konstantin Donhauser, et al. ∙

∙ 01/18/2023

Strong inductive biases provably prevent harmless interpolation

Classical wisdom suggests that estimators should avoid fitting noise to ...

0 Michael Aerni, et al. ∙

∙ 12/07/2022

Tight bounds for maximum ℓ_1-margin classifiers

Popular iterative algorithms such as boosting methods and coordinate des...

0 Stefan Stojanovic, et al. ∙

∙ 03/07/2022

Fast rates for noisy interpolation require rethinking the effects of inductive bias

Good generalization performance on high-dimensional data crucially hinge...

5 Konstantin Donhauser, et al. ∙

∙ 11/10/2021

Tight bounds for minimum l1-norm interpolation of noisy data

We provide matching upper and lower bounds of order σ^2/log(d/n) for the...

0 Guillaume Wang, et al. ∙

∙ 08/05/2021

Interpolation can hurt robust generalization even when there is no noise

Numerous recent works show that overparameterization implicitly reduces ...

0 Konstantin Donhauser, et al. ∙

∙ 04/09/2021

How rotational invariance of common kernels prevents generalization in high dimensions

Kernel ridge regression is well-known to achieve minimax optimal rates i...

0 Konstantin Donhauser, et al. ∙

∙ 03/19/2019

Efficient Smoothing of Dilated Convolutions for Image Segmentation

Dilated Convolutions have been shown to be highly useful for the task of...

0 Thomas Ziegler, et al. ∙

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