Bloem, P., de Rooij, S., & Adriaans, P. (2015). Two problems for sophistication. In K. Chaudhuri, C. Gentile, & S. Zilles (Eds.), Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015 : proceedings (pp. 379-394). (Lecture Notes in Computer Science; Vol. 9355), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-319-24486-0_25[details]
de Vries, G. K. D., & de Rooij, S. (2015). Substructure counting graph kernels for machine learning from RDF data. Journal of Web Semantics, 35(2), 71-84. Advance online publication. https://doi.org/10.1016/j.websem.2015.08.002[details]
2014
Bloem, P., Mota, F., de Rooij, S., Antunes, L., & Adriaans, P. (2014). A safe approximation for Kolmogorov complexity. In P. Auer, A. Clark, T. Zeugman, & S. Zilles (Eds.), Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014 : proceedings (pp. 336-350). (Lecture Notes in Computer Science; Vol. 8776), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-319-11662-4_24[details]
de Vries, G. K. D., & de Rooij, S. (2013). A Fast and Simple Graph Kernel for RDF. In C. d'Amato, P. Berka, V. Svátek, & K. Wecel (Eds.), Proceedings of the International Workshop on Data Mining on Linked Data, with Linked Data Mining Challenge: collocated with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013) : Prague, Czech Republic, September 23, 2013 (CEUR Workshop Proceedings; Vol. 1082). CEUR-WS. http://ceur-ws.org/Vol-1082/paper2.pdf[details]
de Rooij, S., & Vitányi, P. M. B. (2012). Approximating rate-distortion graphs of individual data: Experiments in lossy compression and denoising. IEEE Transactions on Computers, 61(3), 395-407. https://doi.org/10.1109/TC.2011.25[details]
2010
Koolen, W. M., & de Rooij, S. (2010). Switching investments. In M. Hutter, F. Stephan, V. Vovk, & T. Zeugmann (Eds.), Algorithmic Learning Theory: 21st international conference, ALT 2010, Canberra, Australia, October 6-8, 2010 : proceedings (pp. 239-254). (Lecture Notes in Computer Science; Vol. 6331), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-642-16108-7_21[details]
2008
Koolen, W. M., & de Rooij, S. (2008). Combining expert advice efficiently. In R. Servedio, & T. Zhang (Eds.), Proceedings of the 21st Annual Conference on Learning Theory (pp. 275-286). Omnipress. http://www.learningtheory.org/colt2008/82-Koolen.pdf[details]
van Erven, T. A. L., Grünwald, P. D., & de Rooij, S. (2007). Catching Up Faster in Bayesian Model Selection and Model Averaging. In Advances in Neural Information Processing Systems (pp. 417-424). Neural Information Processing Systems (NIPS) Foundation. [details]
van Erven, T. A. L., de Rooij, S., & Grünwald, P. (2007). Switching between Predictors with an Application in Density Estimation. In Proceedings of the 28th Symposium on Information Theory in the Benelux (pp. 149-156). SAS. [details]
2005
de Rooij, S. (2005). MDL Model Selection using the ML Plug-in Code. In the proceedings of the International Symposium on Information Theory (ISIT) [details]
Ó Nualláin, B. S., & de Rooij, S. (2004). Online Suffix Trees with Counts. In Proceedings of the Data Compression Conference 2004 IEEE Computer Society Press. [details]
2008
Koolen, W. M., & de Rooij, S. (2008). Combining Expert Advice Efficiently. In A. Nijholt, M. Pantic, M. Poel, & H. Hondorp (Eds.), Proceedings of the twentieth Belgian-Dutch Conference on Artificial Intelligence: Enschede, October 30-31, 2008 (pp. 323-324). (BNAIC; Vol. 2008). University of Twente. http://eprints.eemcs.utwente.nl/13354/01/bnaic2008-proceedings.pdf[details]
Spreker
de Rooij, S. (speaker) (2006). Algorithmic Rate Distortion, INS1 Seminar, CWI, Amsterdam.
de Rooij, S. (speaker) (2006). Algorithmic Rate Distortion in Practice, Dagstuhl seminar on Kolmogorov Complexity.
de Rooij, S. (speaker) (2006). Asymptotic Log-loss of Prequential Maximum Likelihood Codes, ITSTAT.
2016
Bloem, P. (2016). Single sample statistics: Exercises in learning from just one example. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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