Empirical Inference Statistical Learning Theory Article Design and Analysis of the NIPS 2016 Review Process Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U. Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (Published) arXiv URL BibTeX

Statistical Learning Theory Conference Paper Measures of distortion for machine learning Vankadara, L., von Luxburg, U. In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , 2018 BibTeX

Statistical Learning Theory Conference Paper Practical Methods for Graph Two-Sample Testing Ghoshdastidar, D., von Luxburg, U. In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , 2018 BibTeX

Statistical Learning Theory Conference Paper Comparison-based nearest neighbor search Haghiri, S., Ghoshdastidar, D., von Luxburg, U. In Artificial Intelligence and Statistics, Artificial Intelligence and Statistics (AISTATS), 2017 BibTeX

Statistical Learning Theory Conference Paper Kernel functions based on triplet comparisons Kleindessner, M., von Luxburg, U. In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2017), 2017 BibTeX

Statistical Learning Theory Article Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis Kleindessner, M., von Luxburg, U. Journal of Machine Learning Research (JMLR), Journal of Machine Learning Research, 18, 2017 BibTeX

Statistical Learning Theory Conference Paper Two-sample tests for large random graphs using network statistics Ghoshdastidar, D., Gutzeit, M., Carpentier, A., von Luxburg, U. In Conference on Computational Learning Theory (COLT), Conference on Computational Learning Theory (COLT), 2017 BibTeX

Empirical Inference Conference Paper Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines Sajjadi, M. S. M., Alamgir, M., von Luxburg, U. Proceedings of the 3rd ACM conference on Learning @ Scale, 369-378, (Editors: Haywood, J. and Aleven, V. and Kay, J. and Roll, I.), ACM, L@S, April 2016, (An earlier version of this paper had been presented at the ICML 2015 workshop for Machine Learning for Education.) (Published) Arxiv Peer-Grading dataset request BibTeX

Empirical Inference Conference Paper Peer grading in a course on algorithms and data structures Sajjadi, M. S. M., Alamgir, M., von Luxburg, U. Workshop on Machine Learning for Education (ML4Ed) at the 32th International Conference on Machine Learning (ICML), 2015 (Published) Arxiv BibTeX

Empirical Inference Conference Paper Peer grading in a course on algorithms and data structures Sajjadi, M. S. M., Alamgir, M., von Luxburg, U. Workshop on Crowdsourcing and Machine Learning (CrowdML) Workshop on Machine Learning for Education (ML4Ed) at at the 32th International Conference on Machine Learning (ICML), 2015 (Published) Arxiv BibTeX