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ORCIDUlf Brefeld
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- affiliation: Leuphana University of Lüneburg, Germany
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2020 – today
- 2024
[i7]Ulf Brefeld, Jesse Davis, Laura de Jong, Stephanie Kovalchik:
Computational Approaches to Strategy and Tactics in Sports (Dagstuhl Seminar 24081). Dagstuhl Reports 14(2): 164-181 (2024)- 2023
- [j17]Valéria Quadros dos Reis
, Maria E. R. Rabello, Anderson C. Lima, Guilherme P. S. Jardim, Eraldo R. Fernandes, Ulf Brefeld:
Data practices in apps from Brazil: What do privacy policies inform us about? J. Interact. Syst. 14(1): 1-8 (2023) - [c55]Noha A. Sarhan, Christian Wilms, Vanessa Closius, Ulf Brefeld, Simone Frintrop:
Hands in Focus: Sign Language Recognition Via Top-Down Attention. ICIP 2023: 2555-2559 - [c54]Jennifer Jorina Matthiesen, Hanne Hastedt, Ulf Brefeld:
User Authentication via Multifaceted Mouse Movements and Outlier Exposure. IDA 2023: 300-313 - [e10]Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Machine Learning and Data Mining for Sports Analytics - 9th International Workshop, MLSA 2022, Grenoble, France, September 19, 2022, Revised Selected Papers. Communications in Computer and Information Science 1783, Springer 2023, ISBN 978-3-031-27526-5 [contents] - 2022
- [j16]Uwe Dick, Daniel Link, Ulf Brefeld:
Who can receive the pass? A computational model for quantifying availability in soccer. Data Min. Knowl. Discov. 36(3): 987-1014 (2022) - [c53]Yannick Rudolph, Ulf Brefeld:
Modeling Conditional Dependencies in Multiagent Trajectories. AISTATS 2022: 10518-10533 - [c52]Dennis Fassmeyer, Pascal Fassmeyer, Ulf Brefeld:
Semi-Supervised Generative Models for Multiagent Trajectories. NeurIPS 2022 - [e9]Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Machine Learning and Data Mining for Sports Analytics - 8th International Workshop, MLSA 2021, Virtual Event, September 13, 2021, Revised Selected Papers. Communications in Computer and Information Science 1571, Springer 2022, ISBN 978-3-031-02043-8 [contents] - 2021
- [j15]Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen:
Joint optimization of an autoencoder for clustering and embedding. Mach. Learn. 110(7): 1901-1937 (2021) - [c51]Samuel G. Fadel, Sebastian Mair, Ricardo da Silva Torres, Ulf Brefeld:
Principled Interpolation in Normalizing Flows. ECML/PKDD (2) 2021: 116-131 - [i6]Ulf Brefeld, Jesse Davis, Martin Lames, James J. Little:
Machine Learning in Sports (Dagstuhl Seminar 21411). Dagstuhl Reports 11(9): 45-63 (2021) - 2020
- [c50]Jennifer Jorina Matthiesen, Ulf Brefeld:
Assessing User Behavior by Mouse Movements. HCI (38) 2020: 68-75 - [c49]Yannick Rudolph, Ulf Brefeld, Uwe Dick:
Graph Conditional Variational Models: Too Complex for Multiagent Trajectories? ICBINB@NeurIPS 2020: 136-147 - [e8]Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Machine Learning and Data Mining for Sports Analytics - 7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings. Communications in Computer and Information Science 1324, Springer 2020, ISBN 978-3-030-64911-1 [contents] - [e7]Ulf Brefeld, Élisa Fromont, Andreas Hotho, Arno J. Knobbe, Marloes H. Maathuis, Céline 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 Computer Science 11906, Springer 2020, ISBN 978-3-030-46149-2 [contents] - [e6]Ulf Brefeld, Élisa Fromont, Andreas Hotho, Arno J. Knobbe, Marloes H. Maathuis, Céline 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 Computer Science 11907, Springer 2020, ISBN 978-3-030-46146-1 [contents] - [e5]Ulf Brefeld, Élisa Fromont, Andreas Hotho, Arno J. Knobbe, Marloes H. Maathuis, Céline 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 Computer Science 11908, Springer 2020, ISBN 978-3-030-46132-4 [contents] - [i5]Samuel G. Fadel, Sebastian Mair, Ricardo da Silva Torres, Ulf Brefeld:
Principled Interpolation in Normalizing Flows. CoRR abs/2010.12059 (2020) - [i4]Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen:
Joint Optimization of an Autoencoder for Clustering and Embedding. CoRR abs/2012.03740 (2020)
2010 – 2019
- 2019
- [j14]Irina Pandarova, Torben Schmidt, Johannes Hartig, Ahcène Boubekki, Roger Dale Jones, Ulf Brefeld:
Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring. Int. J. Artif. Intell. Educ. 29(3): 342-367 (2019) - [j13]Uwe Dick, Ulf Brefeld:
Learning to Rate Player Positioning in Soccer. Big Data 7(1): 71-82 (2019) - [j12]Ulf Brefeld, Jan Lasek, Sebastian Mair:
Probabilistic movement models and zones of control. Mach. Learn. 108(1): 127-147 (2019) - [c48]Maryam Tavakol, Tobias Joppen, Ulf Brefeld, Johannes Fürnkranz:
Personalized Transaction Kernels for Recommendation Using MCTS. KI 2019: 338-352 - [c47]Sebastian Mair, Ulf Brefeld:
Coresets for Archetypal Analysis. NeurIPS 2019: 7245-7253 - [e4]Ulf Brefeld, Edward Curry, Elizabeth Daly, Brian MacNamee, Alice Marascu, Fabio Pinelli, Michele Berlingerio, Neil Hurley:
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, ISBN 978-3-030-10996-7 [contents] - [e3]Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Machine Learning and Data Mining for Sports Analytics - 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings. Lecture Notes in Computer Science 11330, Springer 2019, ISBN 978-3-030-17273-2 [contents] - 2018
- [j11]Sebastian Mair, Ulf Brefeld:
Distributed robust Gaussian Process regression. Knowl. Inf. Syst. 55(2): 415-435 (2018) - [c46]Ahcène Boubekki, Shailee Jain, Ulf Brefeld:
Mining User Trajectories in Electronic Text Books. EDM 2018 - [c45]Radhika Gaonkar, Maryam Tavakol, Ulf Brefeld:
MDP-based Itinerary Recommendation using Geo-Tagged Social Media. IDA 2018: 111-123 - [c44]Sebastian Mair, Yannick Rudolph, Vanessa Closius, Ulf Brefeld:
Frame-Based Optimal Design. ECML/PKDD (2) 2018: 447-463 - 2017
- [j10]Ulf Brefeld, Albrecht Zimmermann:
Guest editorial: Special issue on sports analytics. Data Min. Knowl. Discov. 31(6): 1577-1579 (2017) - [c43]Sebastian Mair, Ahcène Boubekki, Ulf Brefeld:
Frame-based Data Factorizations. ICML 2017: 2305-2313 - [c42]Ahcène Boubekki, Ulf Brefeld, Cláudio Leonardo Lucchesi, Wolfgang Stille:
Propagating Maximum Capacities for Recommendation. KI 2017: 72-84 - [c41]Jan Reubold, Ahcène Boubekki, Thorsten Strufe, Ulf Brefeld:
Infinite Mixtures of Markov Chains. NFMCP@PKDD/ECML 2017: 167-181 - [c40]Maryam Tavakol, Ulf Brefeld:
A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations. ECML/PKDD (2) 2017: 269-284 - 2016
- [j9]Konstantin Knauf, Daniel Memmert, Ulf Brefeld:
Spatio-temporal convolution kernels. Mach. Learn. 102(2): 247-273 (2016) - [j8]Cornelius Arndt, Ulf Brefeld:
Predicting the future performance of soccer players. Stat. Anal. Data Min. 9(5): 373-382 (2016) - [c39]Ahcène Boubekki, Ulf Kröhne, Frank Goldhammer, Waltraud Schreiber, Ulf Brefeld:
Data-Driven Analyses of Electronic Text Books. Solving Large Scale Learning Tasks 2016: 362-376 - [c38]Maryam Tavakol, Hamid Zafartavanaelmi, Ulf Brefeld:
Feature Extraction and Aggregation for Predicting the EURO 2016. MLSA@PKDD/ECML 2016 - 2015
- [c37]Ahcène Boubekki, Ulf Kröhne, Frank Goldhammer, Waltraud Schreiber, Ulf Brefeld:
Toward Data-Driven Analyses of Electronic Text Books. EDM 2015: 592-593 - [c36]Ahcène Boubekki, Ulf Brefeld, Thomas Delacroix:
Generalising IRT to Discriminate Between Examinees. EDM 2015: 604-605 - [c35]Markus Brandt, Ulf Brefeld:
Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer. MLSA@PKDD/ECML 2015: 10-17 - [c34]Ulf Brefeld:
Multi-view learning with dependent views. SAC 2015: 865-870 - 2014
- [c33]Jamal Abdul Nasir, Nico Görnitz, Ulf Brefeld:
An Off-the-shelf Approach to Authorship Attribution. COLING 2014: 895-904 - [c32]Emmanouil Tzouridis, Jamal Abdul Nasir, Ulf Brefeld:
Learning to Summarise Related Sentences. COLING 2014: 1636-1647 - [c31]Daniel Bengs, Ulf Brefeld:
Computer-based Adaptive Speed Tests. EDM 2014: 221-224 - [c30]Ulf Brefeld:
Interdisciplinary Machine Learning. LWA 2014: 6 - [c29]Eraldo R. Fernandes, Ulf Brefeld, Roi Blanco, Jordi Atserias:
Using Wikipedia for Cross-Language Named Entity Recognition. MSM/MUSE/SenseML 2014: 1-25 - [c28]Jens Haase, Ulf Brefeld:
Mining Positional Data Streams. NFMCP 2014: 102-116 - [c27]Maryam Tavakol, Ulf Brefeld:
Factored MDPs for detecting topics of user sessions. RecSys 2014: 33-40 - [i3]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Toward Supervised Anomaly Detection. CoRR abs/1401.6424 (2014) - 2013
- [j7]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Toward Supervised Anomaly Detection. J. Artif. Intell. Res. 46: 235-262 (2013) - [c26]Daniel Bengs, Ulf Brefeld:
Adaptive Speeded Tests. LWA 2013: 86-89 - [c25]Emmanouil Tzouridis, Ulf Brefeld:
Learning Shortest Paths for Word Graphs. LWA 2013: 113-116 - [c24]Jens Haase, Ulf Brefeld:
Finding Similar Movements in Positional Data Streams. MLSA@PKDD/ECML 2013: 49-57 - 2012
- [c23]Peter Haider, Luca Chiarandini, Ulf Brefeld:
Discriminative clustering for market segmentation. KDD 2012: 417-425 - 2011
- [j6]Fabian Rathke, Katja Hansen, Ulf Brefeld, Klaus-Robert Müller:
StructRank: A New Approach for Ligand-Based Virtual Screening. J. Chem. Inf. Model. 51(1): 83-92 (2011) - [j5]Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien:
lp-Norm Multiple Kernel Learning. J. Mach. Learn. Res. 12: 953-997 (2011) - [c22]Giorgos Giannopoulos, Ulf Brefeld, Theodore Dalamagas, Timos K. Sellis:
Learning to rank user intent. CIKM 2011: 195-200 - [c21]Giuseppe Amodeo, Roi Blanco, Ulf Brefeld:
Hybrid models for future event prediction. CIKM 2011: 1981-1984 - [c20]Eraldo R. Fernandes, Ulf Brefeld:
Learning from Partially Annotated Sequences. ECML/PKDD (1) 2011: 407-422 - [c19]Ulf Brefeld, Berkant Barla Cambazoglu, Flavio Paiva Junqueira:
Document assignment in multi-site search engines. WSDM 2011: 575-584 - [i2]Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, Motoaki Kawanabe:
Insights from Classifying Visual Concepts with Multiple Kernel Learning. CoRR abs/1112.3697 (2011) - 2010
- [j4]Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller:
Approximate Tree Kernels. J. Mach. Learn. Res. 11: 555-580 (2010) - [j3]Ulf Brefeld, Lise Getoor, Sofus A. Macskassy:
Eighth workshop on mining and learning with graphs. SIGKDD Explor. 12(2): 63-65 (2010) - [e2]Ulf Brefeld, Lise Getoor, Sofus A. Macskassy:
Proceedings of the Eighth Workshop on Mining and Learning with Graphs, MLG '10, Washington, D.C., USA, July 24-25, 2010. ACM 2010, ISBN 978-1-4503-0214-2 [contents] - [i1]Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien:
Non-Sparse Regularization and Efficient Training with Multiple Kernels. CoRR abs/1003.0079 (2010)
2000 – 2009
- 2009
- [c18]Alexander Binder, Motoaki Kawanabe, Ulf Brefeld:
Efficient Classification of Images with Taxonomies. ACCV (3) 2009: 351-362 - [c17]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Active learning for network intrusion detection. AISec 2009: 47-54 - [c16]Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Pavel Laskov, Klaus-Robert Müller, Alexander Zien:
Efficient and Accurate Lp-Norm Multiple Kernel Learning. NIPS 2009: 997-1005 - [c15]Nico Görnitz, Marius Kloft, Ulf Brefeld:
Active and Semi-supervised Data Domain Description. ECML/PKDD (1) 2009: 407-422 - [c14]Marius Kloft, Shinichi Nakajima, Ulf Brefeld:
Feature Selection for Density Level-Sets. ECML/PKDD (1) 2009: 692-704 - 2008
- [c13]Marius Kloft, Ulf Brefeld, Patrick Düssel, Christian Gehl, Pavel Laskov:
Automatic feature selection for anomaly detection. AISec 2008: 71-76 - [c12]Thoralf Klein, Ulf Brefeld, Tobias Scheffer:
Exact and Approximate Inference for Annotating Graphs with Structural SVMs. ECML/PKDD (1) 2008: 611-623 - 2007
- [b1]Ulf Brefeld:
Semi-supervised structured prediction models. Humboldt University of Berlin, Unter den Linden, Germany, 2007, pp. 1-168 - [c11]Peter Haider, Ulf Brefeld, Tobias Scheffer:
Supervised clustering of streaming data for email batch detection. ICML 2007: 345-352 - [c10]Alexander Zien, Ulf Brefeld, Tobias Scheffer:
Transductive support vector machines for structured variables. ICML 2007: 1183-1190 - [c9]Ulf Brefeld, Thoralf Klein, Tobias Scheffer:
Support Vector Machines for Collective Inference. MLG 2007 - 2006
- [c8]Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel:
Efficient co-regularised least squares regression. ICML 2006: 137-144 - [c7]Ulf Brefeld, Tobias Scheffer:
Semi-supervised learning for structured output variables. ICML 2006: 145-152 - 2005
- [j2]Jörg Hakenberg, Steffen Bickel, Conrad Plake, Ulf Brefeld, Hagen Zahn, Lukas Faulstich, Ulf Leser, Tobias Scheffer:
Systematic feature evaluation for gene name recognition. BMC Bioinform. 6(S-1) (2005) - [c6]Ulf Brefeld, Christoph Büscher, Tobias Scheffer:
Multi-view Discriminative Sequential Learning. ECML 2005: 60-71 - [c5]Ulf Brefeld, Christoph Büscher, Tobias Scheffer:
Multi-View Hidden Markov Perceptrons. LWA 2005: 134-138 - 2004
- [j1]Peter Geibel, Ulf Brefeld, Fritz Wysotzki:
Perceptron and SVM learning with generalized cost models. Intell. Data Anal. 8(5): 439-455 (2004) - [c4]Ulf Brefeld, Tobias Scheffer:
Co-EM support vector learning. ICML 2004 - [c3]Ulf Brefeld, Steffen Bickel, Tobias Scheffer:
Multi-View Lernen. LWA 2004: 131 - [e1]Andreas Abecker, Steffen Bickel, Ulf Brefeld, Isabel Drost, Nicola Henze, Olaf Herden, Mirjam Minor, Tobias Scheffer, Ljiljana Stojanovic, Stephan Weibelzahl:
LWA 2004: Lernen - Wissensentdeckung - Adaptivität, Berlin, 4. - 6. Oktober 2004, Workshopwoche der GI-Fachgruppen/Arbeitskreise (1) Fachgruppe Adaptivität und Benutzermodellierung in Interaktiven Softwaresystemen (ABIS 2004), (2) Arbeitskreis Knowledge Discovery (AKKD 2004), (3) Fachgruppe Maschinelles Lernen (FGML 2004), (4) Fachgruppe Wissens- und Erfahrungsmanagement (FGWM 2004). Humbold-Universität Berlin 2004 [contents] - 2003
- [c2]Ulf Brefeld, Peter Geibel, Fritz Wysotzki:
Support Vector Machines with Example Dependent Costs. ECML 2003: 23-34 - [c1]Peter Geibel, Ulf Brefeld, Fritz Wysotzki:
Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs. IDA 2003: 167-178
Coauthor Index
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last updated on 2024-08-01 21:13 CEST by the dblp team
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