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Statistical Learning Theory Empirical Inference Conference Paper A Bandit Model for Human-Machine Decision Making with Private Information and Opacity Bordt, S., von Luxburg, U. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 151:7300-7319, Proceedings of Machine Learning Research, (Editors: Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel), PMLR, March 2022 (Published) URL BibTeX
Statistical Learning Theory Article Advancing research on unconscious priming: When can scientists claim an indirect task advantage? Meyen, S., Zerweck, I., Amado, C., von Luxburg, U., Franz, V. Journal of Experimental Psychology: General, 151(1):65-81, 2022 (Published) DOI BibTeX
Statistical Learning Theory Article Contextual Cueing May Not Be Unconscious Meyen, S. V. L. U. F. V. H. Perception, 50(1_Suppl):51-51, 2021 (Published) BibTeX
Statistical Learning Theory Article Group decisions based on confidence weighted majority voting Meyen, S., Sigg, D. M. B., von Luxburg, U., Franz, V. H. Cognitive Research: Principles and Implications, 6:18, 2021 (Published) DOI URL BibTeX
Statistical Learning Theory Conference Paper Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models Vankadara, L., Bordt, S., von Luxburg, U., Ghoshdastidar, D. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 3817-3825, Proceedings of Machine Learning Research , PMLR, The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), 2021 (Published) URL BibTeX
Statistical Learning Theory Conference Paper Explaining the Explainer: A First Theoretical Analysis of LIME Garreau, D., von Luxburg, U. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics , 108:1287-1296, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) , August 2020 (Published) URL BibTeX
Statistical Learning Theory Article Estimation of perceptual scales using ordinal embedding Haghiri, S., Wichmann, F. A., von Luxburg, U. Journal of Vision, 20(9), 2020 (Published) DOI URL BibTeX
Statistical Learning Theory Conference Paper Too Relaxed to Be Fair Lohaus, M., Perrot, M., von Luxburg, U. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), 119:6316 - 6325, Proceedings of Machine Learning Research, Curran Associates, Inc. , Red Hook, International Conference of Machine Learning (ICML), 2020 (Published) URL BibTeX
Statistical Learning Theory Article Two-sample Hypothesis Testing for Inhomogeneous Random Graphs Ghoshdastidar, D., Gutzeit, M., Carpentier, A., von Luxburg, U. Annals of Statistics, 48(4):2208-2229, 2020 BibTeX
Statistical Learning Theory Conference Paper NetGAN without GAN: From Random Walks to Low-Rank Approximations Rendsburg, L., Heidrich, H., von Luxburg, U. International Conference of Machine Learning (ICML), 2020 BibTeX
Statistical Learning Theory Conference Paper Foundations of Comparison-Based Hierarchical Clustering Ghoshdastidar, D., Perrot, M., von Luxburg, U. Advances in Neural Information Processing Systems 32 (NIPS 2019), NeurIPS, Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Statistical Learning Theory Conference Paper Boosting for Comparison-Based Learning Perrot, M., von Luxburg, U. International Joint Conference on Artificial Intelligence (IJCAI), 2019 BibTeX
Statistical Learning Theory Conference Paper When do random forests fail? Tang, C., Garreau, D., von Luxburg, U. In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , December 2018 BibTeX
Statistical Learning Theory Conference Paper Comparison-Based Random Forests Haghiri, S., Garreau, D., Luxburg, U. V. International Conference on Machine learning (ICML), 2018 URL BibTeX
Statistical Learning Theory Technical Report NEWMA: a new method for scalable model-free online change-point detection Keriven, N., Garreau, D., Poli, I. 2018 URL BibTeX
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
Empirical Inference Probabilistic Numerics Statistical Learning Theory Miscellaneous Large sample analysis of the median heuristic Garreau, D., Jitkrittum, W., Kanagawa, M. 2018 (In preparation) arXiv 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