activity
20182025
most citedOptional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations

25 citations · 53 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ME2025

Bringing Closure to False Discovery Rate Control: A General Principle for Multiple Testing

Ziyu Xu, Aldo Solari, Lasse Fischer +3

We present a novel necessary and sufficient principle for multiple testing methods controlling an expected loss. This principle asserts that every such multiple testing method is a…

math.ST2025★ 1 cited

The e-Partitioning Principle of False Discovery Rate Control

Jelle Goeman, Rianne de Heide, Aldo Solari

We present a novel necessary and sufficient principle for False Discovery Rate (FDR) control. This e-Partitioning Principle says that a procedure controls FDR if and only if it is…

stat.ML2022★ 2 cited

Top Two Algorithms Revisited

Marc Jourdan, Rémy Degenne, Dorian Baudry +2

Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms. They select t…

cs.LG2021★ 2 cited

Bandits with many optimal arms

Rianne de Heide, James Cheshire, Pierre Ménard +1

We consider a stochastic bandit problem with a possibly infinite number of arms. We write for the proportion of optimal arms and for the minimal mean-gap between optimal…

cs.LG2019★ 5 cited

Fixed-Confidence Guarantees for Bayesian Best-Arm Identification

Xuedong Shang, Rianne de Heide, Emilie Kaufmann +2

We investigate and provide new insights on the sampling rule called Top-Two Thompson Sampling (TTTS). In particular, we justify its use for fixed-confidence best-arm identification…

math.ST2019★ 18 cited

Safe-Bayesian Generalized Linear Regression

Rianne de Heide, Alisa Kirichenko, Nishant Mehta +1

We study generalized Bayesian inference under misspecification, i.e. when the model is 'wrong but useful'. Generalized Bayes equips the likelihood with a learning rate . We show…

math.ST2018★ 25 cited

Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations

Allard Hendriksen, Rianne de Heide, Peter Grünwald

It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary p…