Multi-view clustering. S Bickel, T Scheffer ICDM 4 (2004), 19-26, 2004 | 1030 | 2004 |
Active hidden markov models for information extraction T Scheffer, C Decomain, S Wrobel International symposium on intelligent data analysis, 309-318, 2001 | 745 | 2001 |
Discriminative learning for differing training and test distributions S Bickel, M Brückner, T Scheffer Proceedings of the 24th international conference on Machine learning, 81-88, 2007 | 539 | 2007 |
Discriminative learning under covariate shift. S Bickel, M Brückner, T Scheffer Journal of Machine Learning Research 10 (9), 2009 | 478 | 2009 |
Unsupervised prediction of citation influences L Dietz, S Bickel, T Scheffer Proceedings of the 24th international conference on Machine learning, 233-240, 2007 | 357 | 2007 |
Stackelberg games for adversarial prediction problems M Brückner, T Scheffer Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 294 | 2011 |
Static prediction games for adversarial learning problems M Brückner, C Kanzow, T Scheffer The Journal of Machine Learning Research 13 (1), 2617-2654, 2012 | 271 | 2012 |
Efficient co-regularised least squares regression U Brefeld, T Gärtner, T Scheffer, S Wrobel Proceedings of the 23rd international conference on Machine learning, 137-144, 2006 | 239 | 2006 |
Finding association rules that trade support optimally against confidence T Scheffer European conference on principles of data mining and knowledge discovery …, 2001 | 232 | 2001 |
Co-EM support vector learning U Brefeld, T Scheffer Proceedings of the twenty-first international conference on Machine learning, 16, 2004 | 229 | 2004 |
RainNet v1. 0: a convolutional neural network for radar-based precipitation nowcasting G Ayzel, T Scheffer, M Heistermann Geoscientific Model Development 13 (6), 2631-2644, 2020 | 219 | 2020 |
Multi-task learning for HIV therapy screening S Bickel, J Bogojeska, T Lengauer, T Scheffer Proceedings of the 25th international conference on Machine learning, 56-63, 2008 | 213 | 2008 |
AUC maximizing support vector learning U Brefeld, T Scheffer Proceedings of the ICML 2005 workshop on ROC Analysis in Machine Learning, 2005 | 193 | 2005 |
Taxonomic metagenome sequence assignment with structured output models KR Patil, P Haider, PB Pope, PJ Turnbaugh, M Morrison, T Scheffer, ... Nature methods 8 (3), 191-192, 2011 | 132 | 2011 |
A nonergodic ground‐motion model for California with spatially varying coefficients N Landwehr, NM Kuehn, T Scheffer, N Abrahamson Bulletin of the Seismological Society of America 106 (6), 2574-2583, 2016 | 131 | 2016 |
Dirichlet-enhanced spam filtering based on biased samples S Bickel, T Scheffer Advances in neural information processing systems 19, 2006 | 116 | 2006 |
Finding association rules that trade support optimally against confidence T Scheffer Intelligent Data Analysis 9 (4), 381-395, 2005 | 116 | 2005 |
Thwarting the nigritude ultramarine: Learning to identify link spam I Drost, T Scheffer European Conference on Machine Learning, 96-107, 2005 | 113 | 2005 |
Nash equilibria of static prediction games M Brückner, T Scheffer Advances in neural information processing systems 22, 2009 | 104 | 2009 |
Semi-supervised learning for structured output variables U Brefeld, T Scheffer Proceedings of the 23rd international conference on Machine learning, 145-152, 2006 | 103 | 2006 |