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Tim van Erven
Tim van Erven
Associate professor at the University of Amsterdam, the Netherlands
Dirección de correo verificada de uva.nl - Página principal
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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
16362014
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
2132014
A second-order bound with excess losses
P Gaillard, G Stoltz, T Van Erven
Conference on Learning Theory, 176-196, 2014
1672014
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
1112015
Second-order quantile methods for experts and combinatorial games
WM Koolen, T Van Erven
Conference on Learning Theory, 1155-1175, 2015
1102015
Metagrad: Multiple learning rates in online learning
T Van Erven, WM Koolen
Advances in Neural Information Processing Systems 29, 2016
982016
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
762015
Rényi divergence and majorization
T van Erven, P Harremoës
2010 IEEE International Symposium on Information Theory, 1335-1339, 2010
702010
Adaptive hedge
T Erven, WM Koolen, S Rooij, P Grünwald
Advances in Neural Information Processing Systems 24, 2011
582011
Follow the leader with dropout perturbations
T Van Erven, W Kotłowski, MK Warmuth
Conference on Learning Theory, 949-974, 2014
562014
Catching up faster in Bayesian model selection and model averaging
T Erven, S Rooij, P Grünwald
Advances in Neural Information Processing Systems 20, 2007
552007
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
452016
The many faces of exponential weights in online learning
D Hoeven, T Erven, W Kotłowski
Conference On Learning Theory, 2067-2092, 2018
402018
Learning the learning rate for prediction with expert advice
WM Koolen, T Van Erven, P Grünwald
Advances in neural information processing systems 27, 2014
322014
Lipschitz adaptivity with multiple learning rates in online learning
Z Mhammedi, WM Koolen, T Van Erven
Conference on Learning Theory, 2490-2511, 2019
282019
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
252014
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
212021
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
192020
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