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.

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

- Stochastics seminar, Otto von Güricke Universität Magdeburg, November 2019

*Safe Testing* - Google DeepMind, Paris, July 2019

*Safe Testing* - Sequel Seminar, Inria Lille, June 2019

*Safe Testing* - Formal Epistemology Workshop , Turin, June 2019

*On the truth-convergence of open-minded Bayesianism* - Scientific Meeting, CWI Amsterdam, April 2018

*Hypothesis Testing*

- Machine Learning Theory (MasterMath, the joint universities national Dutch Master's Programme in Mathematics), Fall 2019

together with Wouter Koolen and Peter Grünwald. I do the first half on Statistical Learning Theory.

- Machine Learning Theory (MasterMath, the joint universities national Dutch Master's Programme in Mathematics), Fall 2018

together with Wouter Koolen and Peter Grünwald. I did the first half on Statistical Learning Theory. - Lectures on online learning and bandits in Peter Grünwald's Information Theoretic Learning course (MSc Mathematics, Leiden University), Spring 2018
- Lecture on Information Theory in Machine Learning in Christian Schaffner's Information Theory course (MSc Logic, University of Amsterdam), Fall 2017
- Machine Learning Theory (MasterMath, the joint universities national Dutch Master's Programme in Mathematics), Fall 2017

together with Wouter Koolen and Peter Grünwald. I did the first half on Statistical Learning Theory. - Lecture on online learning in Peter Grünwald's Information Theoretic Learning course (MSc Mathematics, Leiden University), Spring 2017

- Information Theoretic Learning (MSc Mathematics, Leiden University), Spring 2017, 2018
- Mathematics for Statisticians (MSc Statistical Science, Leiden University), Fall 2013, 2014, 2015

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

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