2012

• Kareem Amin, Michael Kearns, Peter Key, and Anton Schwaighofer, Budget Optimization for Sponsored Search:Censored Learning in MDPs, in Uncertainty in Artificial Intelligence (UAI), Uncertainty in Artificial Intelligence (UAI), August 2012

2009

• Anton Schwaighofer, Joaquin Quinonero Candela, Thomas Borchert, Thore Graepel, and Ralf Herbrich, Scalable Clustering and Keyword Suggestion for Online Advertisements, in Proceedings of ADKDD 2009: 3rd Annual International Workshop on Data Mining and Audience Intelligence for Advertising, Association for Computing Machinery, Inc., 2009
• J. Quiñonero Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, Dataset Shift in Machine Learning, MIT Press, 2009

2009

• Schwaighofer, Anton, Schroeter, Timon, Mika, Sebastian, Blanchard, and Gilles, How Wrong Can We Get? A Review of Machine Learning Approaches and Error Bars, in Combinatorial Chemistry and High Throughput Screening, 2009

2007

• Neil D. Lawrence, Anton Schwaighofer, and Joaquin Quiñonero Candela, Gaussian Processes in Practice, 2007
• Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, and Klaus-Robert Müller, Machine Learning Models for Lipophilicity and their Domain of Applicability, in Molecular Pharmaceutics., vol. 4, no. 4, pp. 524–538, 2007
• Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, and Klaus-Robert Müller, Predicting Lipophilicity of Drug Discovery Molecules using Gaussian Process Models, in ChemMedChem, vol. 2, no. 9, pp. 1265–1267, 2007
• Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, and Klaus-Robert Müller, Estimating the Domain of Applicability for Machine Learning QSAR RModels: A Study on Aqueous Solubility of Drug Discovery Molecules, in Journal of Computer Aided Molecular Design, 2007
• Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Julian Laub, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, and Klaus-Robert Müller, Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach, in Journal of Chemical Information and Modelling, vol. 47, no. 2, pp. 407–424, 2007
• Anton Schwaighofer, Mathäus Dejori, Volker Tresp, and Martin Stetter, Structure Learning with Nonparametric Decomposable Models, in Artificial Neural Networks – ICANN 2007, Springer Verlag, 2007

2006

• Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Sülzle, and Nikolaus Heinrich, Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach, in American Chemical Society 232nd National Meeting, September 2006

2005

• Anton Schwaighofer, Volker Tresp, and Kai Yu, Learning Gaussian Process Kernels via Hierarchical Bayes, in Advances in Neural Information Processing Systems 17, MIT Press, 2005
• Kai Yu, Volker Tresp, and Anton Schwaighofer, Learning Gaussian Processes from Multiple Tasks, in Machine Learning: Proceedings of the 22nd International Conference (ICML 2005), ACM, 2005

2004

• Mathäus Dejori, Anton Schwaighofer, Volker Tresp, and Martin Stetter, Mining Functional Modules in Genetic Networks with Decomposable Graphical Models, in OMICS A Journal of Integrative Biology, vol. 8, no. 2, pp. 176–188, 2004
• Anton Schwaighofer, Marian Grigoraş, Volker Tresp, and Clemens Hoffmann, GPPS: A Gaussian Process Positioning System for Cellular Networks, in Advances in Neural Information Processing Systems 16, MIT Press, 2004
• Kai Yu, Anton Schwaighofer, Volker Tresp, Xiaowei Xu, and Hans-Peter Kriegel, Probabilistic Memory Based Collaborative Filtering: Learning Individual and Social Preferences, in IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 1, pp. 56–69, 2004

2003

• Anton Schwaighofer, Kernel Systems for Regression and Graphical Modelling, October 2003
• Anton Schwaighofer, Volker Tresp, Peter Mayer, Andreas Krause, Jürgen Beuthan, Helmut Rost, Gerhard Metzger, Gerhard A. Müller, and Alexander K. Scheel, Classification of Rheumatoid Joint Inflammation Based on Laser Imaging, in IEEE Transactions on Biomedical Engineering, vol. 50, no. 3, pp. 375–382, 2003
• Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, and Gerhard A. Müller, The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging, in Advances in Neural Information Processing Systems 15, MIT Press, 2003
• Anton Schwaighofer and Volker Tresp, Transductive and Inductive Methods for Approximate Gaussian Process Regression, in Advances in Neural Information Processing Systems 15, MIT Press, 2003
• Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma, and HongJiang Zhang, Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes, in Uncertainty in Artificial Intelligence: Proceedings of the 19th Conference (UAI-2003), Morgan Kaufmann, 2003

2002

• Kai Yu, Xiaowei Xu, Anton Schwaighofer, Volker Tresp, and Hans-Peter Kriegel, Removing Redundancy and Inconsistency in Memory-based Collaborative Filtering, in Proceedings of the 11th International Conference on Information and Knowledge Management CIKM02, ACM, 2002

2001

• Volker Tresp and Anton Schwaighofer, Scalable Kernel Systems, in Artificial Neural Networks – ICANN 2001, Springer Verlag, 2001
• Anton Schwaighofer and Volker Tresp, The Bayesian Committee Support Vector Machine, in Artificial Neural Networks – ICANN 2001, Springer Verlag, 2001

2000

• Anton Schwaighofer, Fingerprint Matching with Spectral Features, May 2000