Rianne de Heide

Machine Learning Group, CWI
Science Park 123, Amsterdam
heide at cwi dot nl

Statistical Science Group, Mathematical Institute - Leiden University

I am a PhD candidate at CWI under supervision of Peter Grünwald. I work on problems and solutions in the intersection of machine learning and statistics, with a focus on hypothesis testing and sequential learning. At the moment I'm working on a new framework for hypothesis testing with Wouter Koolen and Peter Grünwald, and Bayesian inference under model misspecification with Peter Grünwald and Alisa Kirichenko. I'm also interested in the mathematical and philosophical foundations of Bayesianism, machine learning, statistics and probability theory, and I'm working with Tom Sterkenburg on the foundations of Bayesian inference.

I'm a board member of the European Women in Mathematics – The Netherlands, the Dutch association of women professional mathematicians.

In spring 2019, I visited SequeL ( Inria Lille-Nord Europe) on a LUF travel grant, to work with Emilie Kaufmann and Michal Valko.


Invited discussion to the paper Using Stacking to Average Bayesian Predictive Distributions by Yao, Vehtari, Simpson and Gelman
Peter Grünwald and Rianne de Heide
Bayesian Analysis 13 (2018), no. 3, 917-1003.


Fixed-Confidence Guarantees for Bayesian Best-Arm Identification
Xuedong Shang, Rianne de Heide, Emilie Kaufman, Pierre Ménard and Michal Valko
HAL-02330187, arXiv 1910.10945, 2019; Accepted to AISTATS 2020

Safe-Bayesian Generalized Linear Regression
Rianne de Heide, Alisa Kirichenko, Nishant Mehta and Peter Grünwald
arXiv 1910.09227, 2019; Accepted to AISTATS 2020

Safe Testing
Peter Grünwald, Rianne de Heide and Wouter Koolen
arXiv 1906.07801, 2019

The truth-convergence of open-minded Bayesianism
Tom F. Sterkenburg and Rianne de Heide
Submitted. Preliminary version available on request. 2019
Accepted to the Formal Epistemology Workshop 2019 (60 minutes talk, commentary and discussion), and the biennial conference of the European Philosophy of Science Association.

Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations
Allard Hendriksen, Rianne de Heide and Peter Grünwald
arXiv 1807.09077, 2018. Submitted.

Why optional stopping is a problem for Bayesians
Rianne de Heide and Peter Grünwald
arXiv 1708.08278, 2018. Submitted.


The Safe-Bayesian Lasso (MSc Thesis)
Rianne de Heide, 2016

Selected Talks

Past teaching as a lecturer

Past teaching as an assistant

I obtained a BSc in Mathematics from the University of Groningen and a MSc in Mathematics cum laude from Leiden University. I also hold a Bachelor of Music from the Prins Claus Conservatoire and a Master of Music from the Royal Conservatory of The Hague, both in Classical music - Horn.

Until January 2019 worked one day a week at Leiden University with Jacqueline Meulman as coordinator of the MSc programme Statistical Science.

R Package SafeBayes
Saint Nicholas, Rooks and Graph Matchings
I like cycling, learning languages and classical singing. I'm a contralto, and sing in the VU-Kamerkoor and many side-projects.