Peter Grünwald

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Wouter M. KoolenCentrum Wiskunde & Informatica; University of TwenteVerified email at cwi.nl
Tim van ErvenAssociate professor at the University of Amsterdam, the NetherlandsVerified email at uva.nl
Petri MyllymakiProfessor of Computer Science, University of HelsinkiVerified email at helsinki.fi
Teemu RoosProfessor at University of HelsinkiVerified email at helsinki.fi
Rianne de HeideAssistant professor, Department of Applied Mathematics, University of TwenteVerified email at utwente.nl
Nishant A. MehtaAssistant Professor, University of VictoriaVerified email at uvic.ca
Thijs van OmmenAssistant Professor at Utrecht UniversityVerified email at uu.nl
Alexander LyCentrum Wiskunde & InformaticaVerified email at cwi.nl
Judith ter SchureAmsterdam UMCVerified email at amsterdamumc.nl
Mark PittProfessor, Ohio State UniversityVerified email at osu.edu
Joseph HalpernProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Paul VitanyiCWI and University of AmsterdamVerified email at cwi.nl
Philip DawidEmeritus Professor of Statistics, University of CambridgeVerified email at statslab.cam.ac.uk
Wojciech KotłowskiPoznan University of TechnologyVerified email at cs.put.poznan.pl
Eric-Jan WagenmakersUniversity of AmsterdamVerified email at uva.nl
Richard D. GillEmeritus professor of Mathematical Statistics, Leiden UniversityVerified email at math.leidenuniv.nl
Peter HarremoesLektor i matematik, Niels BrockVerified email at niels.brock.dk
Glenn ShaferProfessor, Rutgers UniversityVerified email at business.rutgers.edu
Aaditya RamdasAssociate Professor (with tenure), Carnegie Mellon UniversityVerified email at stat.cmu.edu
Jay MyungProfessor, Department of Psychology, Ohio State UniversityVerified email at osu.edu

Peter Grünwald

Group Leader Machine Learning, CWI; Professor of Statistics, Leiden University

Verified email at cwi.nl - Homepage

machine learning statistics information theory foundations of statistics e-values


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The minimum description length principle PD Grünwald The MIT Press, 2007	2099	2007
A tutorial introduction to the minimum description length principle P Grunwald Advances in Minimum Description Length - Theory and Methods, 2005	656*	2005
Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory PD Grünwald, AP Dawid	610	2004
Advances in Minimum Description Length: Theory and Applications PD Grünwald, J Myung, MA Pitt Advances in Minimum Description Length: Theory and Applications, 2005	514	2005
Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it P Grünwald, T Van Ommen	324	2017
Shannon information and Kolmogorov complexity P Grunwald, P Vitányi arXiv preprint cs/0410002, 2004	230	2004
Model selection based on minimum description length P Grünwald Journal of mathematical psychology 44 (1), 133-152, 2000	229	2000
Follow the leader if you can, hedge if you must S de Rooij, T Van Erven, PD Grünwald, WM Koolen Journal of Machine Learning Research (JMLR) 15, 1281-1315, 2014	227	2014
Safe testing P Grünwald, R de Heide, WM Koolen arXiv preprint arxiv:1906.07801, 2020	219	2020
The safe Bayesian P Grünwald International Conference on Algorithmic Learning Theory, 169-183, 2012	180	2012
On discriminative Bayesian network classifiers and logistic regression T Roos, H Wettig, P Grünwald, P Myllymäki, H Tirri Machine Learning 59, 267-296, 2005	167	2005
The minimum description length principle and reasoning under uncertainty PD Grünwald Ph.D. thesis, University of Amsterdam, 1998	162	1998
A minimum description length approach to grammar inference P Grünwald International joint conference on artificial intelligence, 203-216, 1995	153	1995
Game-theoretic statistics and safe anytime-valid inference A Ramdas, P Grünwald, V Vovk, G Shafer Statistical Science 38 (4), 576-601, 2023	149	2023
Kolmogorov complexity and information theory. With an interpretation in terms of questions and answers PD Grünwald, PMB Vitányi Journal of Logic, Language and Information 12 (4), 497-529, 2003	145	2003
Fast rates for general unbounded loss functions: from ERM to generalized Bayes PD Grünwald, NA Mehta Journal of Machine Learning Research 21 (56), 1-80, 2020	136*	2020
Accumulative prediction error and the selection of time series models EJ Wagenmakers, P Grünwald, M Steyvers Journal of Mathematical Psychology 50 (2), 149-166, 2006	133	2006
Fast rates in statistical and online learning T Van Erven, P Grunwald, NA Mehta, M Reid, R Williamson Journal of Machine Learning Research 16, 1793-1861, 2015	122	2015
Suboptimal behavior of Bayes and MDL in classification under misspecification P Grünwald, J Langford Machine Learning 66, 119-149, 2007	100*	2007
The statistical strength of nonlocality proofs W Van Dam, RD Gill, PD Grunwald IEEE transactions on information theory 51 (8), 2812-2835, 2005	100	2005

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