| R\'enyi Divergence and Kullback-Leibler Divergence T Van Erven, P Harremoës IEEE Transactions on Information Theory 60 (7), 3797-3820, 2014 | 1878 | 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 | 227 | 2014 |
| A second-order bound with excess losses P Gaillard, G Stoltz, T Van Erven Conference on Learning Theory, 176-196, 2014 | 185 | 2014 |
| 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 | 123 | 2015 |
| Second-order quantile methods for experts and combinatorial games WM Koolen, T Van Erven Conference on Learning Theory, 1155-1175, 2015 | 118 | 2015 |
| 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 |
| Metagrad: Multiple learning rates in online learning T Van Erven, WM Koolen Advances in Neural Information Processing Systems 29, 2016 | 108 | 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 | 81 | 2015 |
| Rényi divergence and majorization T van Erven, P Harremoës 2010 IEEE International Symposium on Information Theory, 1335-1339, 2010 | 71 | 2010 |
| Adaptive hedge T Erven, WM Koolen, S Rooij, P Grünwald Advances in Neural Information Processing Systems 24, 2011 | 64 | 2011 |
| Follow the leader with dropout perturbations T Van Erven, W Kotłowski, MK Warmuth Conference on Learning Theory, 949-974, 2014 | 57 | 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 | 46 | 2016 |
| The many faces of exponential weights in online learning D Hoeven, T Erven, W Kotłowski Conference On Learning Theory, 2067-2092, 2018 | 45 | 2018 |
| Lipschitz adaptivity with multiple learning rates in online learning Z Mhammedi, WM Koolen, T Van Erven Conference on Learning Theory, 2490-2511, 2019 | 35 | 2019 |
| 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 |
| 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 | 28 | 2021 |
| 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 | 26 | 2014 |
| 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 | 24 | 2020 |
Wouter M. KoolenCentrum Wiskunde & Informatica; University of Twentecwi.nl의 이메일 확인됨
