| Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000 | 3156 | 2000 |
| 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 | 2873 | 2009 |
| Gaussian processes for machine learning M Seeger International journal of neural systems 14 (02), 69-106, 2004 | 1447 | 2004 |
| 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 | 1073 | 2012 |
| Deep state space models for time series forecasting SS Rangapuram, MW Seeger, J Gasthaus, L Stella, Y Wang, ... Advances in neural information processing systems 31, 2018 | 884 | 2018 |
| Fast sparse Gaussian process methods: The informative vector machine N Lawrence, M Seeger, R Herbrich Advances in neural information processing systems 15, 2002 | 756 | 2002 |
| Learning with labeled and unlabeled data M Seeger | 741 | 2000 |
| Fast forward selection to speed up sparse Gaussian process regression MW Seeger, CKI Williams, ND Lawrence International Workshop on Artificial Intelligence and Statistics, 254-261, 2003 | 682 | 2003 |
| PAC-Bayesian generalisation error bounds for Gaussian process classification M Seeger Journal of machine learning research 3 (Oct), 233-269, 2002 | 441 | 2002 |
| Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009 | 409 | 2009 |
| Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007 | 384 | 2007 |
| Local Gaussian process regression for real time online model learning D Nguyen-Tuong, J Peters, M Seeger Advances in neural information processing systems 21, 2008 | 351 | 2008 |
| Semiparametric latent factor models YW Teh, M Seeger, MI Jordan International Workshop on Artificial Intelligence and Statistics, 333-340, 2005 | 324 | 2005 |
| Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations M Seeger University of Edinburgh, 2003 | 261 | 2003 |
| Leep: A new measure to evaluate transferability of learned representations C Nguyen, T Hassner, M Seeger, C Archambeau International Conference on Machine Learning, 7294-7305, 2020 | 243 | 2020 |
| The effect of the input density distribution on kernel-based classifiers C Williams, M Seeger Proceedings of the 17th international conference on machine learning, 1159-1166, 2000 | 241 | 2000 |
| Expectation propagation for exponential families M Seeger | 213 | 2005 |
| Computed torque control with nonparametric regression models D Nguyen-Tuong, M Seeger, J Peters 2008 American Control Conference, 212-217, 2008 | 205 | 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 | 189 | 2010 |
| Scalable hyperparameter transfer learning V Perrone, R Jenatton, MW Seeger, C Archambeau Advances in neural information processing systems 31, 2018 | 185 | 2018 |
Cédric ArchambeauDirector of AI at Helsing. Ex Principal Applied Scientist at AWS.helsing.ai의 이메일 확인됨
