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Hans Kersting
Hans Kersting
Research Scientist, Yahoo! Research
Verified email at yahooinc.com - Homepage
Title
Cited by
Cited by
Year
Probabilistic Numerics: Computation as Machine Learning
P Hennig, MA Osborne, HP Kersting
Cambridge University Press, 2022
852022
Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective
F Tronarp, H Kersting, S Särkkä, P Hennig
Statistics and Computing 29, 1297-1315, 2019
632019
Active uncertainty calibration in Bayesian ODE solvers
H Kersting, P Hennig
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial …, 2016
622016
Convergence rates of Gaussian ODE filters
H Kersting, TJ Sullivan, P Hennig
Statistics and computing 30 (6), 1791-1816, 2020
412020
Anticorrelated noise injection for improved generalization
A Orvieto, H Kersting, F Proske, F Bach, A Lucchi
International Conference on Machine Learning, 17094-17116, 2022
382022
Differentiable likelihoods for fast inversion of’likelihood-free’dynamical systems
H Kersting, N Krämer, M Schiegg, C Daniel, M Tiemann, P Hennig
International Conference on Machine Learning, 5198-5208, 2020
232020
Explicit regularization in overparametrized models via noise injection
A Orvieto, A Raj, H Kersting, F Bach
International Conference on Artificial Intelligence and Statistics, 7265-7287, 2023
222023
An sde for modeling sam: Theory and insights
EM Compagnoni, L Biggio, A Orvieto, FN Proske, H Kersting, A Lucchi
International Conference on Machine Learning, 25209-25253, 2023
152023
Bayesian filtering for ODEs with bounded derivatives
E Magnani, H Kersting, M Schober, P Hennig
arXiv preprint arXiv:1709.08471, 2017
102017
On the theoretical properties of noise correlation in stochastic optimization
A Lucchi, F Proske, A Orvieto, F Bach, H Kersting
Advances in Neural Information Processing Systems 35, 14261-14273, 2022
72022
A Fourier state space model for Bayesian ODE filters
H Kersting, M Mahsereci
arXiv preprint arXiv:2007.09118, 2020
62020
Batch size selection by stochastic optimal control
J Zhao, A Lucchi, FN Proske, A Orvieto, H Kersting
Has it Trained Yet? NeurIPS 2022 Workshop, 2022
52022
Uncertainty-Aware Numerical Solutions of ODEs by Bayesian Filtering
H Kersting
Universität Tübingen, 2021
52021
Mean first exit times of Ornstein–Uhlenbeck processes in high-dimensional spaces
H Kersting, A Orvieto, F Proske, A Lucchi
Journal of Physics A: Mathematical and Theoretical 56 (21), 215003, 2023
32023
SDEs for Minimax Optimization
EM Compagnoni, A Orvieto, H Kersting, F Proske, A Lucchi
International Conference on Artificial Intelligence and Statistics, 4834-4842, 2024
2024
SDEs for Minimax Optimization
E Monzio Compagnoni, A Orvieto, H Kersting, FN Proske, A Lucchi
arXiv e-prints, arXiv: 2402.12508, 2024
2024
An SDE for Modeling SAM: Theory and Insights
E Monzio Compagnoni, L Biggio, A Orvieto, FN Proske, H Kersting, ...
arXiv e-prints, arXiv: 2301.08203, 2023
2023
ODE Filters–Forward and Backward
H Kersting
2021
On the Properties of Noise Injection in Stochastic Optimization
A Lucchi, A Orvieto, F Proske, F Bach, H Kersting
European Meeting of Statisticians 2023 Warsaw 3–7 July, 2023 Book of …, 0
Probabilistic Numerics
MA Osborne, HP Kersting
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