Tim van Erven
Tim van Erven
Associate professor at the University of Amsterdam, the Netherlands
Verified email at - Homepage
Cited by
Cited by
R\'enyi Divergence and Kullback-Leibler Divergence
T Van Erven, P Harremoës
IEEE Transactions on Information Theory 60 (7), 3797-3820, 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
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
A second-order bound with excess losses
P Gaillard, G Stoltz, T Van Erven
Conference on Learning Theory, 176-196, 2014
Second-order quantile methods for experts and combinatorial games
WM Koolen, T Van Erven
Conference on Learning Theory, 1155-1175, 2015
Fast rates in statistical and online learning
T Van Erven, P Grunwald, NA Mehta, M Reid, R Williamson
MIT Press, 2015
Metagrad: Multiple learning rates in online learning
T Van Erven, WM Koolen
Advances in Neural Information Processing Systems 29, 2016
Rényi divergence and majorization
T van Erven, P Harremoës
2010 IEEE International Symposium on Information Theory, 1335-1339, 2010
Follow the leader with dropout perturbations
T Van Erven, W Kotłowski, MK Warmuth
Conference on Learning Theory, 949-974, 2014
Adaptive hedge
T Erven, WM Koolen, S Rooij, P Grünwald
Advances in Neural Information Processing Systems 24, 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
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
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
Learning the learning rate for prediction with expert advice
WM Koolen, T Van Erven, P Grünwald
Advances in neural information processing systems 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
Learning the switching rate by discretising Bernoulli sources online
S Rooij, T Erven
Artificial Intelligence and Statistics, 432-439, 2009
PAC-Bayes mini-tutorial: A continuous union bound
T van Erven
arXiv preprint arXiv:1405.1580, 2014
Mixability in statistical learning
T Erven, P Grünwald, MD Reid, RC Williamson
Advances in Neural Information Processing Systems 25, 2012
The many faces of exponential weights in online learning
D Hoeven, T Erven, W Kotłowski
Conference On Learning Theory, 2067-2092, 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
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