| R\'enyi Divergence and Kullback-Leibler Divergence T Van Erven, P Harremoës IEEE Transactions on Information Theory 60 (7), 3797-3820, 2014 | 971 | 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 | 165 | 2014 |
| Catching up faster by switching sooner: A predictive approach to adaptive estimation with an application to the AIC–BIC dilemma T Erven, P Grünwald, S De Rooij Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2012 | 104* | 2012 |
| A second-order bound with excess losses P Gaillard, G Stoltz, T Van Erven Conference on Learning Theory, 176-196, 2014 | 96 | 2014 |
| Second-order quantile methods for experts and combinatorial games WM Koolen, T Van Erven Conference on Learning Theory, 1155-1175, 2015 | 86 | 2015 |
| Fast rates in statistical and online learning T Van Erven, P Grunwald, NA Mehta, M Reid, R Williamson MIT Press, 2015 | 72 | 2015 |
| Metagrad: Multiple learning rates in online learning T Van Erven, WM Koolen Advances in Neural Information Processing Systems 29, 2016 | 60 | 2016 |
| Rényi divergence and majorization T van Erven, P Harremoës 2010 IEEE International Symposium on Information Theory, 1335-1339, 2010 | 58 | 2010 |
| Follow the leader with dropout perturbations T Van Erven, W Kotłowski, MK Warmuth Conference on Learning Theory, 949-974, 2014 | 52 | 2014 |
| Adaptive hedge T Erven, WM Koolen, S Rooij, P Grünwald Advances in Neural Information Processing Systems 24, 2011 | 49 | 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 | 48 | 2007 |
| Game-theoretically optimal reconciliation of contemporaneous hierarchical time series forecasts T Erven, J Cugliari Modeling and stochastic learning for forecasting in high dimensions, 297-317, 2015 | 39 | 2015 |
| 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 | 32 | 2016 |
| Learning the learning rate for prediction with expert advice WM Koolen, T Van Erven, P Grünwald Advances in neural information processing systems 27, 2014 | 27 | 2014 |
| 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 | 21* | 2012 |
| Learning the switching rate by discretising Bernoulli sources online S Rooij, T Erven Artificial Intelligence and Statistics, 432-439, 2009 | 19 | 2009 |
| PAC-Bayes mini-tutorial: A continuous union bound T van Erven arXiv preprint arXiv:1405.1580, 2014 | 18 | 2014 |
| Mixability in statistical learning T Erven, P Grünwald, MD Reid, RC Williamson Advances in Neural Information Processing Systems 25, 2012 | 17 | 2012 |
| The many faces of exponential weights in online learning D Hoeven, T Erven, W Kotłowski Conference On Learning Theory, 2067-2092, 2018 | 16 | 2018 |
| When data compression and statistics disagree: two frequentist challenges for the minimum description length principle TAL van Erven Mathematical Institute, Faculty of Science, Leiden, 2010 | 12 | 2010 |
Wouter M. KoolenCentrum Wiskunde & InformaticaVerified email at cwi.nl
