Matthias Seeger
Matthias Seeger
Principal Applied Scientist, Amazon, Berlin
Verified email at amazon.de - Homepage
Title
Cited by
Cited by
Year
Using the Nyström method to speed up kernel machines
CKI Williams, M Seeger
Advances in neural information processing systems, 682-688, 2001
21132001
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
10932009
Learning with labeled and unlabeled data
M Seeger
6702000
Gaussian processes for machine learning
M Seeger
International journal of neural systems 14 (02), 69-106, 2004
5932004
Fast sparse Gaussian process methods: The informative vector machine
R Herbrich, ND Lawrence, M Seeger
Advances in neural information processing systems, 625-632, 2003
5822003
Fast forward selection to speed up sparse Gaussian process regression
M Seeger, C Williams, N Lawrence
Artificial Intelligence and Statistics 9, 2003
4382003
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
3742012
Bayesian inference and optimal design for the sparse linear model
MW Seeger
Journal of Machine Learning Research 9 (Apr), 759-813, 2008
3262008
Pac-bayesian generalisation error bounds for gaussian process classification
M Seeger
Journal of machine learning research 3 (Oct), 233-269, 2002
2802002
Model learning with local gaussian process regression
D Nguyen-Tuong, M Seeger, J Peters
Advanced Robotics 23 (15), 2015-2034, 2009
2542009
Semiparametric latent factor models
M Seeger, YW Teh, M Jordan
2282005
Local gaussian process regression for real time online model learning
D Nguyen-Tuong, JR Peters, M Seeger
Advances in neural information processing systems, 1193-1200, 2009
2052009
Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations
M Seeger
University of Edinburgh, 2003
2042003
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
2022000
Expectation propagation for exponential families
M Seeger
1682005
Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers
M Seeger
Advances in neural information processing systems, 603-609, 2000
1562000
Computed torque control with nonparametric regression models
D Nguyen-Tuong, M Seeger, J Peters
2008 American Control Conference, 212-217, 2008
1372008
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
1272010
Fast gaussian process regression using kd-trees
Y Shen, M Seeger, AY Ng
Advances in neural information processing systems, 1225-1232, 2006
1232006
Compressed sensing and Bayesian experimental design
MW Seeger, H Nickisch
Proceedings of the 25th international conference on Machine learning, 912-919, 2008
1182008
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Articles 1–20