| Using the Nyström method to speed up kernel machines C Williams, M Seeger Proceedings of the 14th annual conference on neural information processing …, 2001 | 2296 | 2001 |
| Gaussian process optimization in the bandit setting: No regret and experimental design N Srinivas, A Krause, SM Kakade, M Seeger arXiv preprint arXiv:0912.3995, 2009 | 1305 | 2009 |
| Learning with labeled and unlabeled data M Seeger | 709 | 2000 |
| Gaussian processes for machine learning M Seeger International journal of neural systems 14 (02), 69-106, 2004 | 659 | 2004 |
| Fast sparse Gaussian process methods: The informative vector machine N Lawrence, M Seeger, R Herbrich Proceedings of the 16th annual conference on neural information processing …, 2003 | 627 | 2003 |
| Fast forward selection to speed up sparse Gaussian process regression M Seeger, C Williams, N Lawrence Artificial Intelligence and Statistics 9, 2003 | 492 | 2003 |
| Information-theoretic regret bounds for gaussian process optimization in the bandit setting N Srinivas, A Krause, SM Kakade, MW Seeger IEEE Transactions on Information Theory 58 (5), 3250-3265, 2012 | 428 | 2012 |
| Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007 | 354 | 2007 |
| PAC-Bayesian generalisation error bounds for Gaussian process classification M Seeger Journal of machine learning research 3 (Oct), 233-269, 2002 | 306 | 2002 |
| Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009 | 279 | 2009 |
| Semiparametric latent factor models M Seeger, YW Teh, M Jordan | 253 | 2005 |
| Local gaussian process regression for real time online model learning and control D Nguyen-Tuong, J Peters, M Seeger Proceedings of the 21st International Conference on Neural Information …, 2008 | 228 | 2008 |
| The effect of the input density distribution on kernel-based classifiers C Williams, M Seeger Proceedings of the 17th international conference on machine learning, 2000 | 214 | 2000 |
| Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations M Seeger University of Edinburgh, 2003 | 213 | 2003 |
| Expectation propagation for exponential families M Seeger | 182 | 2005 |
| Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers M Seeger Proceedings of the 13th Annual Conference on Neural Information Processing …, 2000 | 165 | 2000 |
| Computed torque control with nonparametric regression models D Nguyen-Tuong, M Seeger, J Peters 2008 American Control Conference, 212-217, 2008 | 145 | 2008 |
| Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design M Seeger, H Nickisch, R Pohmann, B Schölkopf Magnetic Resonance in Medicine: An Official Journal of the International …, 2010 | 137 | 2010 |
| Fast gaussian process regression using kd-trees Y Shen, A Ng, M Seeger Proceedings of the 19th Annual Conference on Neural Information Processing …, 2006 | 131 | 2006 |
| Deep state space models for time series forecasting SS Rangapuram, M Seeger, J Gasthaus, L Stella, Y Wang, ... Proceedings of the 32nd international conference on neural information …, 2018 | 125 | 2018 |
Jan PetersProfessor for Intelligent Autonomous Systems at Technische Universität DarmstadtVerified email at ias.tu-darmstadt.de
