Tim van Erven 个人学术档案

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引用	3128	2266
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Wouter M. KoolenCentrum Wiskunde & Informatica; University of Twente在 cwi.nl 的电子邮件经过验证
Peter GrünwaldGroup Leader Machine Learning, CWI; Professor of Statistics, Leiden University在 cwi.nl 的电子邮件经过验证
Wojciech KotłowskiPoznan University of Technology在 cs.put.poznan.pl 的电子邮件经过验证
Mark D. ReidApple在 reid.name 的电子邮件经过验证
Robert C. WilliamsonUniversity of Tübingen在 uni-tuebingen.de 的电子邮件经过验证
Dirk van der HoevenLeiden University在 dirkvanderhoeven.com 的电子邮件经过验证
Hedi HADIJICentraleSupelec在 centralesupelec.fr 的电子邮件经过验证
Sarah SachsPh.D. student at University of Amsterdam在 uva.nl 的电子邮件经过验证
Jairo CugliariUniversité Lumière Lyon 2在 univ-lyon2.fr 的电子邮件经过验证
Pierre GaillardINRIA在 gaillard.me 的电子邮件经过验证
Gilles StoltzCNRS / Université Paris-Saclay / HEC Paris在 hec.fr 的电子邮件经过验证
Peter HarremoesLektor i matematik, Niels Brock在 niels.brock.dk 的电子邮件经过验证
Nishant A. MehtaAssistant Professor, University of Victoria在 uvic.ca 的电子邮件经过验证
Manfred K. Warmuthvisiting Google Brain在 google.com 的电子邮件经过验证
Jack J. MayoKorteweg-de Vries Institute for Mathematics, University of Amsterdam在 uva.nl 的电子邮件经过验证
Cristobal GuzmanPontificia Universidad Católica de Chile在 mat.uc.cl 的电子邮件经过验证
Hidde FokkemaPhD student, University of Amsterdam在 uva.nl 的电子邮件经过验证
Zakaria MhammediMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
Rianne de HeideAssistant professor, Mathematics department, Vrije Universiteit Amsterdam在 vu.nl 的电子邮件经过验证
Botond SzabóAssociate Professor, Bocconi University在 unibocconi.it 的电子邮件经过验证

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Tim van Erven

Associate professor at the University of Amsterdam, the Netherlands

在 uva.nl 的电子邮件经过验证 - 首页

Online Learning Statistical Learning Theory Explainable Machine Learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
R\'enyi Divergence and Kullback-Leibler Divergence T Van Erven, P Harremoës IEEE Transactions on Information Theory 60 (7), 3797-3820, 2014	1617	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	211	2014
A second-order bound with excess losses P Gaillard, G Stoltz, T Van Erven Conference on Learning Theory, 176-196, 2014	165	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	69	2010
Follow the leader with dropout perturbations T Van Erven, W Kotłowski, MK Warmuth Conference on Learning Theory, 949-974, 2014	56	2014
Adaptive hedge T Erven, WM Koolen, S Rooij, P Grünwald Advances in Neural Information Processing Systems 24, 2011	56	2011
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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