Thore Graepel
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Thore Graepel is a person.
References
2017
- (Silver et al., 2017) ⇒ David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis. (2017). “Mastering the Game of Go Without Human Knowledge.” In: Nature, 550(7676).
2016
- (Silver et al., 2016) ⇒ David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. (2016). “Mastering the Game of Go with Deep Neural Networks and Tree Search.” In: Nature, 529(7587). doi:10.1038/nature16961
2010
- (Graepel et al., 2010) ⇒ Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert, and Ralf Herbrich. (2010). “Web-scale Bayesian Click-through Rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine.” In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010).
2008
- (Graepel et al., 2008) ⇒ Thore Graepel, and Ralf Herbrich. (2008). “Large Scale Data Analysis and Modelling in Online Services and Advertising.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401892
2000
- (Herbrich et al., 2000) ⇒ Ralf Herbrich, Thore Graepel, and Klaus Obermayer. (2000). “Large Margin rank boundaries for ordinal regression." MIT Press.
1999a
- (Herbrich et al., 1999) ⇒ Ralf Herbrich, Thore Graepel, and Klaus Obermayer. (1999). “Support Vector Learning for Ordinal Regression.” In: Proceedings of the Ninth International Conference on Artificial Neural Networks.
1999b
- (Herbrich et al., 1999) ⇒ Ralf Herbrich, Thore Graepel, and Klaus Obermayer. (1999). “Support Vector Learning for Ordinal Regression.” In: Proceedings of the Ninth International Conference on Artificial Neural Networks.