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04/26/2022
PAC-Bayes training for neural networks: sparsity and uncertainty quantification
We study the Gibbs posterior distribution from PAC-Bayes theory for spar...
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03/17/2022
Dimensionality Reduction and Wasserstein Stability for Kernel Regression
In a high-dimensional regression framework, we study consequences of the...
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08/11/2021
Dispersal density estimation across scales
We consider a space structured population model generated by two point c...
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02/26/2021
Nonparametric calibration for stochastic reaction-diffusion equations based on discrete observations
Nonparametric estimation for semilinear SPDEs, namely stochastic reactio...
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10/02/2019
Parameter estimation for SPDEs based on discrete observations in time and space
Parameter estimation for a parabolic, linear, stochastic partial differe...
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01/04/2019
On central limit theorems for power variations of the solution to the stochastic heat equation
We consider the stochastic heat equation whose solution is observed disc...
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01/30/2018
Bayesian inverse problems with unknown operators
We consider the Bayesian approach to linear inverse problems when the un...
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10/30/2017