Tim van Erven

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Wouter M. KoolenCentrum Wiskunde & Informatica; University of TwenteVerified email at cwi.nl
Peter GrünwaldGroup Leader Machine Learning, CWI; Professor of Statistics, Leiden UniversityVerified email at cwi.nl
Wojciech KotłowskiPoznan University of TechnologyVerified email at cs.put.poznan.pl
Mark D. ReidAppleVerified email at reid.name
Robert C. WilliamsonUniversity of TübingenVerified email at uni-tuebingen.de
Dirk van der HoevenLeiden UniversityVerified email at dirkvanderhoeven.com
Hedi HADIJICentraleSupelecVerified email at centralesupelec.fr
Sarah SachsPh.D. student at University of AmsterdamVerified email at uva.nl
Jairo CugliariUniversité Lumière Lyon 2Verified email at univ-lyon2.fr
Pierre GaillardINRIAVerified email at gaillard.me
Gilles StoltzCNRS / Université Paris-Saclay / HEC ParisVerified email at hec.fr
Peter HarremoesLektor i matematik, Niels BrockVerified email at niels.brock.dk
Nishant A. MehtaAssistant Professor, University of VictoriaVerified email at uvic.ca
Manfred K. Warmuthvisiting Google BrainVerified email at google.com
Jack J. MayoKorteweg-de Vries Institute for Mathematics, University of AmsterdamVerified email at uva.nl
Cristobal GuzmanPontificia Universidad Católica de ChileVerified email at mat.uc.cl
Hidde FokkemaPhD student, University of AmsterdamVerified email at uva.nl
Zakaria MhammediMassachusetts Institute of TechnologyVerified email at mit.edu
Rianne de HeideAssistant professor, Mathematics department, Vrije Universiteit AmsterdamVerified email at utwente.nl
Rajiv KhannaAssistant Prof, PurdueCSVerified email at purdue.edu

Tim van Erven

Associate professor at the University of Amsterdam, the Netherlands

Verified email at uva.nl - Homepage

Online Learning Statistical Learning Theory Explainable Machine Learning


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R\'enyi Divergence and Kullback-Leibler Divergence T Van Erven, P Harremoës IEEE Transactions on Information Theory 60 (7), 3797-3820, 2014	1643	2014
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 15, 1281-1316, 2014	213	2014
A second-order bound with excess losses P Gaillard, G Stoltz, T Van Erven Conference on Learning Theory, 176-196, 2014	167	2014
Catching up faster by switching sooner: A predictive approach to adaptive estimation with an application to the AIC–BIC dilemma T Van Erven, P Grünwald, S De Rooij Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2012	114*	2012
Fast rates in statistical and online learning T Van Erven, PD Grünwald, NA Mehta, MD Reid, RC Williamson The Journal of Machine Learning Research 16 (1), 1793-1861, 2015	111	2015
Second-order quantile methods for experts and combinatorial games WM Koolen, T Van Erven Conference on Learning Theory, 1155-1175, 2015	110	2015
Metagrad: Multiple learning rates in online learning T Van Erven, WM Koolen Advances in Neural Information Processing Systems 29, 2016	98	2016
Game-theoretically optimal reconciliation of contemporaneous hierarchical time series forecasts T Van Erven, J Cugliari Modeling and stochastic learning for forecasting in high dimensions, 297-317, 2015	76	2015
Rényi divergence and majorization T van Erven, P Harremoës 2010 IEEE International Symposium on Information Theory, 1335-1339, 2010	70	2010
Adaptive hedge T Erven, WM Koolen, S Rooij, P Grünwald Advances in Neural Information Processing Systems 24, 2011	58	2011
Follow the leader with dropout perturbations T Van Erven, W Kotłowski, MK Warmuth Conference on Learning Theory, 949-974, 2014	56	2014
Catching up faster in Bayesian model selection and model averaging T Erven, S Rooij, P Grünwald Advances in Neural Information Processing Systems 20, 2007	55	2007
Combining adversarial guarantees and stochastic fast rates in online learning WM Koolen, P Grünwald, T Van Erven Advances in Neural Information Processing Systems 29, 2016	45	2016
The many faces of exponential weights in online learning D Hoeven, T Erven, W Kotłowski Conference On Learning Theory, 2067-2092, 2018	40	2018
Learning the learning rate for prediction with expert advice WM Koolen, T Van Erven, P Grünwald Advances in neural information processing systems 27, 2014	32	2014
Lipschitz adaptivity with multiple learning rates in online learning Z Mhammedi, WM Koolen, T Van Erven Conference on Learning Theory, 2490-2511, 2019	28	2019
Mixability is Bayes Risk Curvature Relative to Log Loss T van Erven, MD Reid, RC Williamson The Journal of Machine Learning Research, 1639-1663, 2012	28*	2012
PAC-Bayes mini-tutorial: A continuous union bound T van Erven arXiv preprint arXiv:1405.1580, 2014	25	2014
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning T van Erven, WM Koolen, D van der Hoeven Journal of Machine Learning Research 22 (161), 1-61, 2021	21	2021
Open problem: Fast and optimal online portfolio selection T Van Erven, D Van der Hoeven, W Kotłowski, WM Koolen Conference on learning theory, 3864-3869, 2020	19	2020

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