Rianne de Heide

Rianne 

Department of Mathematics
NU building, room NU-9A13
Faculty of Science
Vrije Universiteit Amsterdam
De Boelelaan 1111, 1081 HV Amsterdam
E-mail: r.de.heide [at] vu [dot] nl

About me

I am an assistant professor in the Department of Mathematics, at the Vrije Universiteit Amsterdam. I work on problems and solutions in machine learning and statistics. My research focuses on sequential learning, and in particular on (sequential) hypothesis testing, Bayesian learning and best-arm identification problems. Central in my work is both bringing a solid mathematical foundation to the topics I work on in different fields, as well as making the theory accessible for less mathematical audiences. I’m also interested in the mathematical and philosophical foundations of Bayesianism, machine learning, statistics and probability theory.

Find out more.

Veni grant

I am very happy that NWO (the Dutch Research Council) awarded my proposal “E-values for multiple testing” a Veni grant of 280.000 euros. With this I can conduct my own research for the coming 3 years. You can read about my research plans here: https://vu.nl/en/news/2023/veni-grant-for-rianne-de-heide-s-research-into-new-mathematical-theory

(Nederlands: https://vu.nl/nl/nieuws/2023/veni-beurs-voor-onderzoek-rianne-de-heide-naar-nieuwe-wiskundige-theorie)

Cor Baayen Award

I was awarded the 2023 Cor Baayen Early Career Researcher Award (news article). Since I was on pregnancy/maternity leave in the last months of 2023, I will probably receive it and give a talk about my work in the ERCIM spring meeting in 2024.

News

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Research

My research interests include

  • Bandits, Reinforcement Learning

  • (Sequential) hypothesis testing, multiple testing

  • Group invariance in statistics

  • Bayesian methods

  • Learning theory

  • Imprecise probabilities in statistics

  • Foundations of ML, stats and probability theory

Find out more.

Recent Publications

  • Safe Testing
    Peter Grünwald, Rianne de Heide and Wouter Koolen
    Forthcoming as disscussion paper at the JRSS-B, 2024    arxiv preprint RSS
    The discussion meeting took place on January 24, 2024.

  • Attribution-based Explanations that Provide Recourse Cannot be Robust
    Hidde Fokkema, Rianne de Heide and Tim van Erven
    arXiv 2205.15834, 2023   arxiv proc
    Journal of Machine Learning Research, 24(360), pp.1-37.