9 citations · 14 across the 18 of their papers we have counts for
6 papers · 1 filter
Statistical Learning Theory for Distributional Classification
Christian Fiedler
In supervised learning with distributional inputs in the two-stage sampling setup, relevant to applications like learning-based medical screening or causal learning, the inputs (wh…
Kernel conditional tests from learning-theoretic bounds
Pierre-François Massiani, Christian Fiedler, Lukas Haverbeck +2
We propose a framework for hypothesis testing on conditional probability distributions, which we then use to construct statistical tests of functionals of conditional distributions…
On Safety in Safe Bayesian Optimization
Christian Fiedler, Johanna Menn, Lukas Kreisköther +1
Optimizing an unknown function under safety constraints is a central task in robotics, biomedical engineering, and many other disciplines, and increasingly safe Bayesian Optimizati…
On kernel-based statistical learning in the mean field limit
Christian Fiedler, Michael Herty, Sebastian Trimpe
In many applications of machine learning, a large number of variables are considered. Motivated by machine learning of interacting particle systems, we consider the situation when…
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estima…
Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples
Christian Fiedler, Massimo Fornasier, Timo Klock +1
In this paper we approach the problem of unique and stable identifiability of generic deep artificial neural networks with pyramidal shape and smooth activation functions from a fi…