Publications

Rlopt

Published in , 2021

title: “Solving optimal stopping problems with Deep Q-Learning” collection: publications permalink: /publication/2020-24-01-rlopt excerpt: ‘This paper is about application of reinforcement learning in the context of stopping problem in finance.’ date: 2021-01-24 venue: ‘’ paperurl: ‘https://arxiv.org/pdf/2101.09682 .pdf’ citation: ‘Hediger et al. (2019). "On the Use of Random Forest for Two-Sample Testing. " arXiv preprint.’ This paper is about application of reinforcement learning in the context of stopping problem in finance..

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

Published in , 2020

This paper introduces distributional random forests.

Recommended citation: Michel et al. (2020). "High Probability Lower Bounds for the Total Variation Distance. " arXiv preprint. https://arxiv.org/pdf/2005.14458.pdf

High Probability Lower Bounds for the Total Variance Distance

Published in , 2020

This paper introduce a framework for the construction of lower bounds for the total variation distance between two distributions based on samples.

Recommended citation: Michel et al. (2020). "High Probability Lower Bounds for the Total Variation Distance. " arXiv preprint. https://arxiv.org/pdf/2005.06006.pdf

Novel approach to visualize the inter-dependencies between maternal sensitization, breast milk immune components and human milk oligosaccharides in the LIFE Child cohort

Published in , 2020

Here we aimed at visualizing breast milk component complex interactions in the context of the allergy status of the mother or the child.

Recommended citation: Michel et al. (2020). "Novel approach to visualize the inter-dependencies between maternal sensitization, breast milk immune components and human milk oligosaccharides in the LIFE Child cohort. " PLoS ONE, Volume 15. https://doi.org/10.1371/journal.pone.0230472

On the Use of Random Forest for Two-Sample Testing

Published in , 2019

This paper is about two-sample hypothesis test based on the random forest algorithm and out-of-bag error.

Recommended citation: Hediger et al. (2019). "On the Use of Random Forest for Two-Sample Testing. " arXiv preprint. https://arxiv.org/pdf/1903.06287.pdf