I am a professor for Machine Learning at Leuphana Universität Lüneburg. Prior to joining Leuphana, I was joint professor for Knowledge Mining & Assessment at TU Darmstadt and at the Leibniz Institute for Research and Information in Education (DIPF), Frankfurt am Main. Before, I led the Recommender Systems group at Zalando SE and worked on machine learning at Universität Bonn, Yahoo! Research Barcelona, Technische Universität Berlin, Max Planck Institute for Computer Science in Saarbrücken, and at Humboldt-Universität zu Berlin. I received a Diploma in Computer Science in 2003 from Technische Universität Berlin and a Ph.D. (Dr. rer. nat.) in 2008 from Humboldt-Universität zu Berlin.

• Daniel Bengs (DIPF)
• Jennifer Jorina Matthiesen
• Kai Neubauer
• Moritz Wohlstein
• Soham Majumder
• Tino Paulsen
• Yannick Rudolph
• Jakub Michalczyk (STS)

• Prof. Dr. Valéria Reis, Universidade Federal de Mato Grosso do Sul, Brasil

• Dr. Maryam Tavakol (TU Eindhoven)
• Dr. Ahcène Boubekki (UIT Tromsø)
• Dr. Uwe Dick (Sportec Solutions)
• Dr. Sebastian Mair (Uppsala University)
• Dr. Eraldo R. Fernandes (SoundHound)
• Dr. Samuel Fadel (Linköping University)

• Workshop on Sports Analytics (MLSA), ECML/PKDD 2024
• Computational Approaches to Strategy and Tactics in Sports, Dagstuhl, 2024
• Workshop on Sports Analytics (MLSA), ECML/PKDD 2023
• Workshop on Sports Analytics (MLSA), ECML/PKDD 2022
• Workshop on Sports Analytics (MLSA), ECML/PKDD 2021
• Workshop on Sports Analytics (MLSA), ECML/PKDD 2020
• Machine Learning in Sports, Dagstuhl, 2020 (postponed to 2021 due to Covid19)
• Workshop on Sports Analytics (MLSA), ECML/PKDD 2019
• ECML/PKDD 2019, (PC Co-Chair)
• Workshop on Sports Analytics (MLSA), ECML/PKDD 2018
• Nectar Track, ECML/PKDD 2018
• Knowledge Discovery, Data Mining und Maschinelles Lernen, LWDA 2017
• Privacy and Inference, KI 2015
• ESF Exploratory Workshop on Combining Learning and Symbolic Analysis for Software Documentation and Mastering Change, TU Darmstadt 2015
• Industrial Sessions, ECML/PKDD 2013
• Tutorials, ICML 2011
• Demonstration Sessions, ECML/PKDD 2010
• Mining and Learning with Graphs, KDD 2010
• Mining User Generated Content for Security, UCMedia 2009
• High-level Information Extraction, ECML/PKDD 2008
• Learning in Structured Output Spaces, ICML 2006
• Learning, Knowledge Discovery, and Adaptivity, LWA 2004

• M. Wohlstein and U. Brefeld. Toward Learning Distributions of Distributions. Northern Lights Deep Learning Conference 2025.


• Y. Rudolph and U. Brefeld. Masked Autoencoder for Multiagent Trajectories. Machine Learning Journal, 2024. (accepted)


• D. Fassmeyer, M. Cordes, and U. Brefeld. Interactive Sequential Generative Models for Team Sports. Machine Learning Journal, 2024. (accepted)


• T. Paulsen and U. Brefeld. Studying properties of water data using manifold-aware anomaly detectors. Proceedings of the International Conference on the Design of Cyber-Secure Water Plants (DCS-Water’24), 2024.


• Y. Rudolph and U. Brefeld. Convolutional Neural Networks. In D. Memmert (editor): Computer Science in Sport -- Modeling, Simulation, Data Analysis and Visualization of Sports-related Data. Springer, 2024.


• Y. Rudolph and U. Brefeld. Convolutional Neural Networks. In D. Memmert (editor): Sportinformatik -- Modellbildung, Simulation, Datenanalyse und Visualisierung von sportbezogenen Daten. Springer, 2024.


• U. Brefeld. Soccer Analytics - Analysing Tracking Data (abstract). In A. Baca and J. Exel (editors): 13th World Congress of Performance Analysis of Sport and 13th International Symposium on Computer Science in Sport. Springer, 2023.


• N. Sarhan, C. Wilms, V. Closius, U. Brefeld, and S. Frintrop. Hands in focus: Sign language recognition via top-down attention. In Proceedings of the IEEE International Conference on Image Processing, 2023.


• T. Paulsen, S. Fadel, and U. Brefeld. Exploring the Poincare ellipsis. In Proceedings of the ECML PKDD Workshop on Mining and Learning with Graphs, 2023.


• Y. Rudolph and U. Brefeld. Masked autoencoder pretraining for event classification in elite soccer. In Proceedings of the ECML PKDD Workshop on Machine Learning and Data Minig for Sports Analytics, 2023.


• J.J. Matthiesen, H. Hastedt, U. Brefeld. User Authentication via Multifaceted Mouse Movements and Outlier Exposure. Proceedings of Intelligent Data Analysis, 2023.


• V. Quadros, G. Jardim, M. Rabello, A. Lima, E. Fernandes, U. Brefeld. Data practices in apps from Brazil: What do privacy policies inform us about? Journal on Interactive Systems, 2023 (accepted)


• U. Brefeld, J. Davis, J. Van Haaren, A. Zimmermann (eds.), Machine Learning and Data Mining for Sports Analytics, Proceedings of the 9th International Workshop MLSA 2022, CCIS 1783, Springer, 2023


• D. Fassmeyer, P. Fassmeyer, U. Brefeld. Semi-Supervised Generative Models for Multiagent Trajectories. NeurIPS, 2022.


• G. Jardim, M. Rabello, A. Lima, U. Brefeld, and V. Reis. Uma Caracterização das Políticas de Privacidade Utilizadas em Aplicativos no Brasil. In Anais do III Workshop sobre as Implicações da Computação na Sociedade, 13-25, 2022


• U. Brefeld, J. Davis, J. Van Haaren, A. Zimmermann (eds.), Machine Learning and Data Mining for Sports Analytics, Proceedings of the 8th International Workshop MLSA 2021, CCIS 1571, Springer, 2022


• U. Dick, D. Link, and U. Brefeld. Who Can Receive The Pass? - A Computational Model for Quantifying Availability in Soccer. Data Mining and Knowledge Discovery, 2022.


• U. Dick and U. Brefeld. Action rate models for predicting actions in soccer. AStA Advances in Statistical Analysis, 2022.


• Y. Rudolph and U. Brefeld. Modeling Conditional Dependencies in Multiagent Trajectories. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.


• G. Anzer, U. Brefeld, P. Bauer, D. Fassmeyer. Detection of tactical patterns using semi-supervised graph neural networks, MIT Sloan Sports Analytics Conference, 2022 (Winner of Research Paper Competition)


• J. J. Matthiesen, U. Brefeld. When more is less: Adverse Effects in Outlier Exposure. The Northern Lights Deep Learning Conference, 2022.


• Y. Rudolph, S. Fadel, S. Mair, U. Brefeld. Studying the Propagation of Information in VAE Decoders. The Northern Lights Deep Learning Conference, 2022.


• D. Fassmeyer, G. Anzer, P. Bauer, U. Brefeld. Toward Automatically Labeling Situations in Soccer. Frontiers in Sports and Active Living, Elite Sports and Performance Enhancement, 2021, accepted.


• Z. Abedjan, T. Bendig, U. Brefeld, J. Bürkle, J. Desel, S. Edlich, T. Eppler, M. Goedicke, N. Hachmeister, J. Heidrich, S. Höppner, S. M. Kast, D. Krupka, K. Lang, P. Liggesmeyer, J. Meisner, I. Scholtes, M. Tropmann-Frick. Empfehlungen für Masterstudiengänge "Data Science" - Auf Basis eines Bachelors in (Wirtschafts-) Informatik oder Mathematik. Gesellschaft für Informatik, 2021.


• S. G. Fadel, S. Mair, R. da Silva Torres, and U. Brefeld. Contextual Movement Models based on Normalizing Flows. AStA Advances in Statistical Analysis, 2021.


• S. G. Fadel, S. Mair, R. da Silva Torres, and U. Brefeld. Principled Interpolation in Normalizing Flows. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2021.


• G. Anzer, P. Bauer and U. Brefeld. The Origins of Goals in the German Bundesliga. Journal of Sport Sciences, 2021.


• U. Dick, M. Tavakol and U. Brefeld. Rating Player Actions in Soccer. Frontiers in Sports and Active Living, 3, 2021.


• F. Martens, U. Dick and U. Brefeld. Space and control in soccer. Front. Sports Act. Living, 3, 2021.


• A. Boubekki, M. Kampfmeyer, U. Brefeld, R. Jenssen. Joint Optimization of an Autoencoder for Clustering and Embedding. Machine Learning, 110, pages 1901–1937, 2021.


• U. Brefeld, J. Davis, J. Van Haaren, A. Zimmermann (eds.), Machine Learning and Data Mining for Sports Analytics, Proceedings of the 7th International Workshop MLSA 2020, CCIS, volume 1324, Springer, 2020


• U. Brefeld, J. Lasek, S. Mair. Analyzing Positional Data. In C. Ley, Y. Dominicy (eds.), Science Meets Sports - When Statistics Are More Than Numbers, Cambridge Scholars Publishing, pp 81-94, 2020.


• Y. Rudolph, U. Brefeld, and U. Dick. Graph Conditional Variational Models: Too Complex for Multiagent Trajectories? NeurIPS, I Can't Believe It's Not Better! Workshop, 2020.


• C. T. Ekstrøm, H. Van Eetvelde, C. Ley, U. Brefeld. Evaluating one-shot tournament predictions. Journal of Sports Analytics, 2020 (accepted).


• D. Bengs, U. Kröhne, U. Brefeld. Simultaneous Constrained Adaptive Item Selection for Group-Based Testing. Journal of Educational Measurement, 2020.


• J. J. Matthiesen, U. Brefeld. Assessing User Behavior by Mouse Movements. International Conference on Human-Computer Interaction, p68-75, 2020.


• U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, C. Robardet. Machine Learning and Knowledge Discovery in Databases -- European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I, Lecture Notes in Artificial Intelligence 11906, Springer, 2020.


• U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, C. Robardet. Machine Learning and Knowledge Discovery in Databases -- European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II, Lecture Notes in Artificial Intelligence 11907, Springer, 2020.


• U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, C. Robardet. Machine Learning and Knowledge Discovery in Databases -- European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III, Lecture Notes in Artificial Intelligence 11908, Springer, 2020.


• D. Bengs, U. Brefeld, U. Kröhne, and F. Zehner. Incorporating Classification Errors of Automatically Scored Open-Ended Items into IRT Models. Proceedings of the Conference on Frontier Research in Educational Measurement (FREMO), 2020.


• Z. Abedjan, U. Brefeld, J. Bürkle, J. Desel, S. Edlich, T. Eppler, M. Goedicke, J. Heidrich, S. Höppner, S. M. Kast, D. Krupka, K. Lang, P. Liggesmeyer, M. Tropmann-Frick. Data Science: Lern- und Ausbildungsinhalte. GI/PLS White Paper, 2020.


• S. Mair, S. G. Fadel, R. da Silva Torres, and U. Brefeld. Efficient Normalizing Flows to Polytopes. Proceedings of the Northern Lights Deep Learning Workshop, 2020.


• S. G. Fadel, S. Mair, R. da Silva Torres, and U. Brefeld. An Appropriate Prior Distribution for Interpolating Latent Samples in Flow-based Generative Models. Proceedings of the Northern Lights Deep Learning Workshop, 2020.


• K. Neubauer and U. Brefeld. Simultaneously Learning Competencies and Item Difficulties. Companion Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), 2020.


• C. T. Ekstrøm, H. Van Eetvelde, C. Ley, U. Brefeld. Evaluating one-shot tournament predictions. arXiv:1912.07364, 2019.


• S. Mair and U. Brefeld. Coresets for Archtypal Analysis. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019. [GitHub]


• M. Tavakol, T. Joppen, U. Brefeld and J. Fürnkranz. Personalized Transaction Kernels for Recommendation using MCTS. Proceedings of the German Conference on Artificial Intelligence (KI), 2019.


• W. Schreiber, W. Wagner, U. Trautwein, U. Brefeld. Zur empirischen Beforschung des mBooks Belgien: Die Chancen eines Methodenmix. In Kühberger, Bernahrd, Bramann (Eds.), Das Geschichtsschulbuch: Lehren - Lernen- Forschen. Salzburger Beitrüge zur Lehrer/innen/bildung: Der Dialog der Fachdidaktiken mit Fach- und Bildungswissenschaften, Band 6, Waxmann, 57-80, 2019.


• I. Pandarova, T. Schmidt, J. Hartig, A. Boubekki, R. D. Jones, U. Brefeld. Predicting the difficulty of exercise items for dynamic difficulty adaptation in adaptive language tutoring. International Journal of Artificial Intelligence in Education, 29(3), 342-367, 2019.


• W. Schreiber, U. Trautwein, W. Wagner, and U. Brefeld: Reformstudie Belgien: eine Effektstudie zur Einführung von kompetenzorientiertem Rahmenplan und mBook. In Schreiber, Ziegler, Kühberger (Eds.), Geschichtsdidaktischer Zwischenhalt, Waxmann, 2019.


• U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, N. Hurley (eds.): Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III. Lecture Notes in Computer Science 11053, Springer, 2019


• U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli (eds.): Foreword to applied data science, demo, and nectar tracks. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III. Lecture Notes in Computer Science 11053, Springer, 2019


• U. Dick and U. Brefeld. Learning to Rate Player Positioning in Soccer. Big Data, Special Issue on Sports Analytics, 7(1) p 71-82, 2019.


• R. Gaonkar, M. Tavakol, U. Brefeld. MDP-based Itinerary Recommendation using Geo-Tagged Social Media. Proceedings of the Seventeenth International Symposium on Intelligent Data Analysis, 2018.


• U. Brefeld, J. Davis, J. Van Haaren, A. Zimmermann (eds.): Machine Learning and Data Mining for Sports Analytics -- 5th International Workshop, MLSA 2018, colocated with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Revised Selected Papers. Lecture Notes in Artificial Intelligence 11330, Springer, 2019.


• S. Mair, Y. Rudolph, V. Closius, and U. Brefeld. Frame-based optimal design. In Proceedings of the European Conference on Machine Learning, 2018.


• S. Mair and U. Brefeld. Exploiting the Frame for Active Learning in Multi-class Classification (abstract). ICML Workshop on Geometry in Machine Learning, 2018.


• R. Gaonkar, M. Tavakol, U. Brefeld. Recommending Travel Itineraries using Social Media (Abstract). European Conference on Data Analysis, 2018.


• U. Brefeld, J. Lasek, and S. Mair. Probabilistic Movement Models and Zones of Control. Machine Learning Journal, Special Issue on Soccer Analytics, 2018. (accepted)


• A. Boubekki, S. Jain, and U. Brefeld. Mining User Trajectories in Electronic Text Books. Proceedings of Educational Data Mining, 2018.


• D. Bengs, U. Brefeld, and U. Kröhne. Concurrent Adaptive Tests for Formative Assessments in School Classes (Abstract). European Congress of Methodology, 2018.


• D. Bengs, U. Brefeld, and U. Kröhne. Adaptive item selection under matroid constraints. Journal of Computerized Adaptive Testing, 6(2): 15-36, 2018.


• J. Reubold, A. Boubekki, T. Strufe, and U. Brefeld. Infinite Mixtures of Markov Chains. New Frontiers in Mining Complex Patterns, LNAI 10785, Springer, 2018.


• U. Dick and U. Brefeld. Learning to rate player actions in multi-agent scenarios (Abstract). Northern Lights Deep Learning Workshop, 2018.


• S. Mair and U. Brefeld. Distributed Robust Gaussian Process Regression. Knowl. Inf. Syst. 55(2): 415-435, 2018.


• U. Brefeld and A. Zimmermann. Guest Editorial: Special Issue on Sports Analytics. Data Min. Knowl. Discov. 31(6): 1577-1579, 2017.


• J. Reubold, A. Boubekki, T. Strufe, and U. Brefeld. Bayesian User Behavior Models. Proceedings of the ECML/PKDD Workshop on New Frontiers in Mining Complex Patterns, 2017.


• S. Mair, A. Boubekki, and U. Brefeld. Frame-based Matrix Factorizations (abstract). Proceedings of the LWDA Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), 2017.


• S. Mair, A. Boubekki, and U. Brefeld. Frame-based Data Factorizations. Proceedings of the International Conference on Machine Learning, 2017.


• M. Tavakol and U. Brefeld. A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations. Proceedings of the European Conference on Machine Learning, 2017.


• A. Boubekki, C. L. Lucchesi, U. Brefeld, and W. Stille. Propagating Maximum Capacities for Recommendation. Proceedings of the German Conference on Artificial Intelligence, 2017.


• U. Dick and U. Brefeld. Learning to Rate Player Actions on the Example of Soccer (abstract). Proceedings of the MathSport International 2017 Conference, 2017.


• M. Tavakol, H. Zafartavanaelmi, and U. Brefeld. Feature Extraction and Aggregation for Predicting the Euro 2016. ECML/PKDD Workshop on Machine Learning and Data Mining for Sports Analytics, 2016.


• A. Boubekki, U. Kröhne, F. Goldhammer, W. Schreiber, and U. Brefeld. In S. Michaelis, N. Piatkowski, and M. Stolpe (Eds.): Data-Driven Analyses of Electronic Text Books. Solving Large Scale Learning Tasks. Challenges and Algorithms -- Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday. Lecture Notes in Artificial Intelligence 9580, Springer, 362-376, 2016. [link]


• C. Arndt and U. Brefeld. Predicting the Future Performance of Soccer Players. Statistical Analysis and Data Mining, Special Issue on Sports Analytics, 9(5), 373–382, 2016.


• E. R. Fernandes, U. Brefeld, R. Blanco, and J. Atserias. Using Wikipedia for Cross-language Named Entity Recognition. Big Data Analytics in the Social and Ubiquitous Context. Volume 9546 of the series Lecture Notes in Computer Science, Springer, 1-25, 2016.


• K. Knauf, D. Memmert, and U. Brefeld. Spatio-temporal Convolution Kernels. Machine Learning Journal 102(02), 247-273, 2016. [MLJ]


• M. Brandt and U. Brefeld. Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer. Proceedings of the ECML/PKDD Workshop on Machine Learning and Data Mining for Sports Analytics, 2015.


• D. Bengs, U. Kröhne, and U. Brefeld. Optimal Greedy Item Selection for Constrained Tests. International Association for Computerized Adaptive Testing (IACAT) Conference, 2015.


• A. Boubekki, U. Kröhne, F. Goldhammer, W. Schreiber, and U. Brefeld. Toward Data-Driven Analyses of Electronic Text Books. Proceedings of the International Conference on Educational Data Mining, 2015.


• A. Boubekki, U. Brefeld, and T. Delacroix. Generalising IRT to Discriminate Between Examinees. Proceedings of the International Conference on Educational Data Mining, 2015.


• J. Haase and U. Brefeld. Mining Positional Data Streams. In A. Appice, M Ceci, C. Loglisci, G. Manco, E. Masciari, Z. W. Ras (Editors), New Frontiers in Mining Complex Patterns, Springer, 2015.


• U. Brefeld. Multi-View Learning with Dependent Views. Proceedings of the ACM/SIGAPP Symposium on Applied Computing, 2015.


• J. Haase and U. Brefeld. Mining Positional Data Streams. Proceedings of the ECML/PKDD Workshop on New Frontiers in Mining Complex Patterns, 2014.


• U. Brefeld. Structured Prediction in Social Contexts (Keynote). Proceedings of the ECML/PKDD Workshop on Mining Ubiquitous and Social Environments, 2014.


• K. Knauf and U. Brefeld. Spatio-temporal Convolution Kernels for Clustering Trajectories. Proceedings of the KDD Workshop on Large-scale Sports Analytics, 2014.


• U. Brefeld. Interdisciplinary Machine Learning (Keynote). Proceedings of the Conference on Learning, Knowledge and Adaptation (LWA), 2014.


• M. Tavakol and U. Brefeld. Factored MDPs for Detecting the Topic of User Sessions. Proceedings of the ACM Conference on Recommender Systems, 2014.


• E. Tzouridis, J. A. Nasir, and U. Brefeld. Learning to Summarise Related Sentences. Proceedings of the International Conference on Computational Linguistics, 2014.


• J. A. Nasir, N. Görnitz, and U. Brefeld. An Off-the-shelf Approach to Authorship Attribution. Proceedings of the International Conference on Computational Linguistics, 2014.


• J. Kühnhausen, U. Brefeld, T. Reinelt, and C. Gawrilow. Using Accelerometer Data to Predict Hyperactivity in Children (Abstract). EUNETHYDIS International Conference on ADHD, 2014.


• D. Bengs and U. Brefeld. Computer-based Adaptive Speed Tests. Proceedings of the International Conference on Educational Data Mining, 2014.


• J. Kühnhausen, U. Brefeld, T. Reinelt, und C. Gawrilow. Erkennung von Hyperaktivität auf Basis von Bewegungsdaten (Abstract). Symposium Erkennung und Förderung von Verhalten, Stimmung und Kognition durch körperliche Bewegung, Tagung der Gesellschaft für Empirische Bildungsforschung (GEBF), 2014. 


• E. Tzouridis and U. Brefeld. Learning Shortest Paths in Word Graphs. Proceedings of the German Workshop on Knowledge Discovery and Machine Learning (KDML), 2013.


• D. Bengs and U. Brefeld. Adaptive Speed Tests. Proceedings of the German Workshop on Knowledge Discovery and Machine Learning (KDML), 2013.


• J. Haase and U. Brefeld. Finding Similar Movements in Positional Data Streams. Proceedings of the ECML/PKDD Workshop on Machine Learning and Data Mining for Sports Analytics, 2013.


• E. Tzouridis and U. Brefeld. Learning Shortest Paths for Word Graphs. Proceedings of the ECML/PKDD Workshop on Mining Ubiquitous and Social Environments, 2013.


• D. Bengs and U. Brefeld. A Learning Agent for Parameter Adaptation in Speeded Tests. Proceedings of the ECML/PKDD Workshop on Reinforcement Learning from Generalized Feedback: Beyond Numeric Reward, 2013.


• N. Görnitz, M. Kloft, K. Rieck, and U. Brefeld. Toward Supervised Anomaly Detection. Journal of Artificial Intelligence Research, Volume 46, pages 235-262, 2013.


• P. Haider, L. Chiarandini, U. Brefeld, and A. Jaimes. Contextual Models for User Interaction on the Web. ECML/PKDD Workshop on Mining and Exploiting Interpretable Local Patterns (I-PAT), 2012.


• A. Binder, S. Nakajima, M. Kloft, C. Müller, W. Samek, U. Brefeld, K.-R. Müller, M. Kawanabe. Insights from Classifying Visual Concepts with Multiple Kernel Learning, PLoS ONE, 7(8):e38897, 2012.


• P. Haider, L. Chiarandini, and U. Brefeld. Discriminative Clustering for Market Segmentation. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2012.


• A. Binder, S. Nakajima, M. Kloft, C. Müller, W. Samek, U. Brefeld, K.-R. Müller, M. Kawanabe. Insights from Classifying Visual Concepts with Multiple Kernel Learning. ArXiv:1112.3697, 2011.


• P. Haider, L. Chiarandini, and U. Brefeld. Behavioral User Models for Yahoo! News. TechPulse, 2011.


• G. Giannopoulos, U. Brefeld, T. Dalamagas, and T. Sellis. Ranking Models for User Intent. Proceedings of the ACM Conference on Information and Knowledge Management. 2011.


• G. Amodeo, R. Blanco, and U. Brefeld. Hybrid Models for Future Event Prediction. Proceedings of the ACM Conference on Information and Knowledge Management. 2011.


• E. R. Fernandes and U. Brefeld. Learning from Partially Annotated Sequences. Proceedings of the European Conference on Machine Learning. 2011.


• M. Kloft, U. Brefeld, S. Sonnenburg, and A. Zien. lp-Norm Multiple Kernel Learning. Journal of Machine Learning Research, 12(Mar):953-997, 2011.


• U. Brefeld, B. B. Cambazoglu, and F. P. Junqueira. Document Assignment in Multi-Site Search Engines. Proceedings of the International Conference on Web Search and Data Mining, 2011.


• F. Rathke, K. Hansen, U. Brefeld, and K.-R. Müller. StructRank: A New Approach for Ligand-Based Virtual Screening. Journal of Chemical Information Modeling, 51, 83-92 , 2011.


• U. Brefeld, L. Getoor, S. A. Macskassy. Eighth workshop on mining and learning with graphs. SIGKDD Explorations 12(2): 63-65, 2010


• U. Brefeld, L. Getoor, and S. A. Macskassy (Editors). MLG 2010: Proceedings of the Eighth Workshop on Mining and Learning with Graphs, ACM, New York, NY, USA, 2010. [ACM]


• M. Kloft, U. Brefeld, S. Sonnenburg, and A. Zien. Non-Sparse Regularization and Efficient Training with Multiple Kernels. Technical Report UCB/EECS-2010-21, EECS Department, University of California, Berkeley, 2010.


• K. Rieck, T. Krueger, U. Brefeld, and K.-R. Müller. Approximate Tree Kernels. Journal of Machine Learning Research, 11:555-580, 2010.


• U. Brefeld, J. Piskorski, and R. Yangarber (Editors). Proceedings of the UCMedia Workshop on Mining User Generated Content for Security, 2009.


• M. Kloft, U. Brefeld, S. Sonnenburg, and A. Zien. Comparing Sparse and Non-sparse Multiple Kernel Learning. Proceedings of the NIPS Workshop on Understanding Multiple Kernel Learning Methods, 2009.


• M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K.-R. Müller, and A. Zien. Efficient and Accurate Lp-norm Multiple Kernel Learning. Advances in Neural Information Processing Systems, 2010. [proof][code]


• S. Nakajima, A. Binder, C. Müller, W. Wojcikiewicz, M. Kloft, U. Brefeld, K.-R. Müller, and M. Kawanabe. Multiple Kernel Learning for Object Classification. Proceedings of the 12th Workshop on Information-based Induction Sciences, 2009.


• N. Görnitz, M. Kloft, K. Rieck, and U. Brefeld. Active Learning for Network Intrusion Detection. Proceedings of the CCS Workshop on Security and Artificial Intelligence, 2009.


• A. Binder, M. Kawanabe, and U. Brefeld. Efficient Classification of Images with Taxonomies. Proceedings of the Asian Conference on Computer Vision, 2009.


• N. Görnitz, M. Kloft, and U. Brefeld. Active and Semi-supervised Data Domain Description. Proceedings of the European Conference on Machine Learning, 2009.


• M. Kloft, S. Nakajima, and U. Brefeld. Feature Selection for Density Level-Sets. Proceedings of the European Conference on Machine Learning, 2009. [code]


• M. Kloft, U. Brefeld, S. Sonnenburg, A. Zien, P. Laskov, and K.-R. Müller. Learning Non-sparse Kernel Mixtures. Proceedings of the PASCAL2 Workshop on Sparsity in Machine Learning and Statistics, 2009.


• M. Kloft, U. Brefeld, P. Laskov, and S. Sonnenburg. Non-sparse Multiple Kernel Learning. Proceedings of the NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels, 2008.


• K. Rieck, U. Brefeld, and T. Krueger. Approximate Kernels for Trees. Technical Report 5/2008, Fraunhofer Institute FIRST, 2008.


• S. Blohm, U. Brefeld, F. Jungermann, and R. Yangarber (Editors). Proceedings of the ECML Workshop on High-level Information Extraction, 2008.


• M. Kloft, U. Brefeld, P. Düssel, C. Gehl, and P. Laskov. Automatic Feature Selection for Anomaly Detection. Proceedings of the CCS Workshop on Security and Artificial Intelligence, 2008.


• T. Klein, U. Brefeld, and T. Scheffer. Exact and Approximate Inference for Annotating Graphs with Structural SVMs. Proceedings of the European Conference on Machine Learning, 2008.


• U. Brefeld. Semi-supervised Structured Prediction Models. Dissertation, Humboldt-Universität zu Berlin, 2008. [edoc]


• U. Brefeld. Cost-based Ranking in Input Output Spaces. Proceedings of the Workshop on Learning from Non-vectorial Data, 2007.


• U. Brefeld, T. Klein, and T. Scheffer. Support Vector Machines for Collective Inference (Abstract). Proceedings of the Workshop on Mining and Learning with Graphs, 2007.


• A. Zien, U. Brefeld, and T. Scheffer. Transductive Support Vector Machines for Structured Variables. Proceedings of the International Conference on Machine Learning, 2007.


• P. Haider, U. Brefeld, and T. Scheffer. Supervised Clustering of Streaming Data for Email Batch Detection. Proceedings of the International Conference on Machine Learning, 2007. (ICML Best Student Paper Award)


• P. Haider, U. Brefeld, and T. Scheffer. Discriminative Identification of Duplicates. Proceedings of the ECML Workshop on Mining and Learning with Graphs, 2006.


• U. Brefeld, T. Joachims, B. Taskar, and E. P. Xing (Editors). Proceedings of the ICML Workshop on Learning in Structured Output Spaces, 2006.


• U. Brefeld and T. Scheffer. Semi-supervised Learning for Structured Output Variables. Proceedings of the International Conference on Machine Learning, 2006.


• U. Brefeld, T. Gärtner, T. Scheffer, and S. Wrobel. Efficient Co-regularised Least Squares Regression. Proceedings of the International Conference on Machine Learning, 2006.


• U. Brefeld, C. Büscher, and T. Scheffer. Multi-view Discriminative Sequential Learning. Proceedings of the European Conference on Machine Learning, 2005. (ECML Best Paper Award)


• U. Brefeld, C. Büscher, and T. Scheffer. Multi-view Hidden Markov Perceptrons. Proceedings of the German Workshop on Machine Learning (FGML), 2005.


• U. Brefeld and T. Scheffer. AUC Maximizing Support Vector Learning. Proceedings of the ICML Workshop on ROC Analysis in Machine Learning, 2005.


• J. Hakenberg, S. Bickel, C. Plake, U. Brefeld, H. Zahn, L. Faulstich, U. Leser, and T. Scheffer. Systematic Feature Evaluation for Gene Name Recognition. BMC Bioinformatics 6(1):S9, 2005. [abstract]


• A. Abecker, S. Bickel, U. Brefeld, I. Drost, N. Henze, O. Herden, M. Minor, T. Scheffer, L. Stojanovic, S. Weibelzahl (Editors). Proceedings of the Workshops on Learning, Knowledge Discovery, and Adaptivity (LWA), 2004.


• U. Brefeld, S. Bickel, T. Scheffer. Multi-View Lernen (Abstract). Proceedings of the German Workshop on Machine Learning (FGML), 2004.


• U. Brefeld and T. Scheffer. Co-EM Support Vector Learning. Proceedings of the International Conference on Machine Learning, 2004.


• P. Geibel, U. Brefeld, and F. Wysotzki. Perceptron and SVM Learning with Generalized Cost Models. Intelligent Data Analysis, 8(5):439-455,2004. [link]


• S. Bickel, U. Brefeld, L. Faulstich, J. Hakenberg, U. Leser, C. Plake, and T. Scheffer. A Support Vector Machine Classifier for Gene Name Recognition. Proceedings of BioCreative / EMBO Workshop: A Critical Assessment of Text Mining Methods in Molecular Biology, 2004.


• U. Brefeld, P. Geibel, and F. Wysotzki. Support Vector Machines with Example Dependent Costs. Proceedings of the European Conference on Machine Learning, 2003.


• P. Geibel, U. Brefeld, and F. Wysotzki. Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs. Proceedings of the International Symposium on Intelligent Data Analysis, 2003.


• U. Brefeld. Kostenabhängiges Lernen mit DIPOL und Support Vector Machines. Diplomarbeit. Technische Universität Berlin, 2003.