Tim van Erven
Tim van Erven
Associate professor at the University of Amsterdam, the Netherlands
Verified email at uva.nl - Homepage
Title
Cited by
Cited by
Year
R\'enyi Divergence and Kullback-Leibler Divergence
T Van Erven, P Harremoës
IEEE Transactions on Information Theory 60 (7), 3797-3820, 2014
7662014
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
1492014
Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC-BIC dilemma [with Discussion]
T Van Erven, P Grünwald, S De Rooij
Journal of the Royal Statistical Society. Series B (Statistical Methodology …, 2012
107*2012
A second-order bound with excess losses
P Gaillard, G Stoltz, T Van Erven
Conference on Learning Theory, 176-196, 2014
832014
Second-order quantile methods for experts and combinatorial games
WM Koolen, T Van Erven
Conference on Learning Theory, 1155-1175, 2015
762015
Fast rates in statistical and online learning
T Van Erven, P Grunwald, NA Mehta, M Reid, R Williamson
MIT Press, 2015
692015
Rényi divergence and majorization
T van Erven, P Harremoës
2010 IEEE International Symposium on Information Theory, 1335-1339, 2010
542010
Metagrad: Multiple learning rates in online learning
T van Erven, WM Koolen
arXiv preprint arXiv:1604.08740, 2016
522016
Follow the leader with dropout perturbations
T Van Erven, W Kotłowski, MK Warmuth
Conference on Learning Theory, 949-974, 2014
492014
Catching Up Faster in Bayesian Model Selection and Model Averaging.
T Van Erven, P Grunwald, S De Rooij
NIPS 20, 417-424, 2007
462007
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
352015
Combining adversarial guarantees and stochastic fast rates in online learning
WM Koolen, P Grünwald, T van Erven
arXiv preprint arXiv:1605.06439, 2016
322016
Adaptive hedge
T Erven, WM Koolen, S Rooij, P Grünwald
Advances in Neural Information Processing Systems 24, 1656-1664, 2011
302011
Learning the learning rate for prediction with expert advice
W Koolen, T Van Erven, P Grunwald
Advances in Neural Information Processing Systems 27 (NIPS 2014), 2294-2302, 2014
282014
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
20*2012
Learning the switching rate by discretising Bernoulli sources online
S Rooij, T Erven
Artificial Intelligence and Statistics, 432-439, 2009
192009
Mixability in statistical learning
T Van Erven, PD Grünwald, MD Reid, RC Williamson
Advances in Neural Information Processing Systems 25 (NIPS 2012), 2012
172012
Pac-bayes mini-tutorial: a continuous union bound
T van Erven
arXiv preprint arXiv:1405.1580, 2014
122014
Adaptive hedge
T Van Erven, P Grünwald, WM Koolen, S De Rooij
arXiv preprint arXiv:1110.6416, 2011
122011
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
112010
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