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Publications of Author

1. John Langford, Matthias Seeger, Nimrod Megiddo
   An Improved Predictive Accuracy Bound for Averaging Classifiers. [Citation Graph (0, 0)][DBLP]
   ICML, 2001, pp:290-297 [Conf]
   
2. Christopher K. I. Williams, Matthias Seeger
   The Effect of the Input Density Distribution on Kernel-based Classifiers. [Citation Graph (0, 0)][DBLP]
   ICML, 2000, pp:1159-1166 [Conf]
   
3. Sham M. Kakade, Matthias Seeger, Dean P. Foster
   Worst-Case Bounds for Gaussian Process Models. [Citation Graph (0, 0)][DBLP]
   NIPS, 2005, pp:- [Conf]
   
4. Neil D. Lawrence, Matthias Seeger, Ralf Herbrich
   Fast Sparse Gaussian Process Methods: The Informative Vector Machine. [Citation Graph (0, 0)][DBLP]
   NIPS, 2002, pp:609-616 [Conf]
   
5. Matthias Seeger
   Covariance Kernels from Bayesian Generative Models. [Citation Graph (0, 0)][DBLP]
   NIPS, 2001, pp:905-912 [Conf]
   
6. Matthias Seeger
   Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers. [Citation Graph (0, 0)][DBLP]
   NIPS, 1999, pp:603-609 [Conf]
   
7. Yirong Shen, Andrew Y. Ng, Matthias Seeger
   Fast Gaussian Process Regression using KD-Trees. [Citation Graph (0, 0)][DBLP]
   NIPS, 2005, pp:- [Conf]
   
8. Christopher K. I. Williams, Matthias Seeger
   Using the Nyström Method to Speed Up Kernel Machines. [Citation Graph (0, 0)][DBLP]
   NIPS, 2000, pp:682-688 [Conf]
   
9. Matthias Seeger
   Gaussian Processes For Machine Learning. [Citation Graph (0, 0)][DBLP]
   Int. J. Neural Syst., 2004, v:14, n:2, pp:69-106 [Journal]
   
10. Matthias Seeger
    PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2002, v:3, n:, pp:233-269 [Journal]
    
11. Matthias Seeger, Sebastian Gerwinn, Matthias Bethge
    Bayesian Inference for Sparse Generalized Linear Models. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:298-309 [Conf]
    
12. Matthias Seeger
    Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:1233-1240 [Conf]
    
13. 
    Learning Inverse Dynamics: a Comparison. [Citation Graph (, )][DBLP]
    
    
14. 
    Workshop summary: Numerical mathematics in machine learning. [Citation Graph (, )][DBLP]
    
    
15. 
    Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. [Citation Graph (, )][DBLP]
    
    
16. 
    Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. [Citation Graph (, )][DBLP]
    
    
17. 
    Local Gaussian Process Regression for Real Time Online Model Learning. [Citation Graph (, )][DBLP]
    
    
18. 
    Gaussian Process Bandits without Regret: An Experimental Design Approach [Citation Graph (, )][DBLP]