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Konstantin Donhauser
Konstantin Donhauser
Verified email at ai.ethz.ch
Title
Cited by
Cited by
Year
Tight bounds for minimum -norm interpolation of noisy data
G Wang, K Donhauser, F Yang
International Conference on Artificial Intelligence and Statistics, 10572-10602, 2022
252022
Fast rates for noisy interpolation require rethinking the effects of inductive bias
K Donhauser, N Ruggeri, S Stojanovic, F Yang
Proceedings of the 39th International Conference on Machine Learning 162 …, 2022
212022
Efficient smoothing of dilated convolutions for image segmentation
T Ziegler, M Fritsche, L Kuhn, K Donhauser
arXiv preprint arXiv:1903.07992, 2019
212019
How rotational invariance of common kernels prevents generalization in high dimensions
K Donhauser, M Wu, F Yang
Proceedings of the 38th International Conference on Machine Learning 139 …, 2021
172021
Interpolation can hurt robust generalization even when there is no noise
K Donhauser, A Tifrea, M Aerni, R Heckel, F Yang
Advances in Neural Information Processing Systems 34, 23465-23477, 2021
112021
Certified private data release for sparse Lipschitz functions
K Donhauser, J Lokna, A Sanyal, M Boedihardjo, R Hönig, F Yang
arXiv preprint arXiv:2302.09680, 2023
5*2023
Strong inductive biases provably prevent harmless interpolation
M Aerni, M Milanta, K Donhauser, F Yang
International Conference on Learning Representations, 2023
32023
Privacy-preserving data release leveraging optimal transport and particle gradient descent
K Donhauser, J Abad, N Hulkund, F Yang
arXiv preprint arXiv:2401.17823, 2024
12024
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
P De Bartolomeis, J Abad, K Donhauser, F Yang
arXiv preprint arXiv:2312.03871, 2023
12023
Tight bounds for maximum -margin classifiers
S Stojanovic, K Donhauser, F Yang
arXiv preprint arXiv:2212.03783, 2022
12022
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