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Publications of Author

1. Jaco Vermaak, Arnaud Doucet, Patrick Pérez
   Maintaining Multi-Modality through Mixture Tracking. [Citation Graph (0, 0)][DBLP]
   ICCV, 2003, pp:1110-1116 [Conf]
   
2. Simon I. Hill, Arnaud Doucet
   Adapting two-class support vector classification methods to many class problems. [Citation Graph (0, 0)][DBLP]
   ICML, 2005, pp:313-320 [Conf]
   
3. Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang
   Fast particle smoothing: if I had a million particles. [Citation Graph (0, 0)][DBLP]
   ICML, 2006, pp:481-488 [Conf]
   
4. Shien-Shin Tham, Arnaud Doucet, Kotagiri Ramamohanarao
   Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo. [Citation Graph (0, 0)][DBLP]
   ICML, 2002, pp:634-641 [Conf]
   
5. Christophe Andrieu, Nando de Freitas, Arnaud Doucet
   Rao-Blackwellised Particle Filtering via Data Augmentation. [Citation Graph (0, 0)][DBLP]
   NIPS, 2001, pp:561-567 [Conf]
   
6. Christophe Andrieu, João F. G. de Freitas, Arnaud Doucet
   Robust Full Bayesian Methods for Neural Networks. [Citation Graph (0, 0)][DBLP]
   NIPS, 1999, pp:379-385 [Conf]
   
7. João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee
   Global Optimisation of Neural Network Models via Sequential Sampling. [Citation Graph (0, 0)][DBLP]
   NIPS, 1998, pp:410-416 [Conf]
   
8. Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, Eric A. Wan
   The Unscented Particle Filter. [Citation Graph (0, 0)][DBLP]
   NIPS, 2000, pp:584-590 [Conf]
   
9. Jaco Vermaak, Simon J. Godsill, Arnaud Doucet
   Sequential Bayesian Kernel Regression. [Citation Graph (0, 0)][DBLP]
   NIPS, 2003, pp:- [Conf]
   
10. Christophe Andrieu, Nando de Freitas, Arnaud Doucet
    Reversible Jump MCMC Simulated Annealing for Neural Networks. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:11-18 [Conf]
    
11. Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart J. Russell
    Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:176-183 [Conf]
    
12. Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan
    An Introduction to MCMC for Machine Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2003, v:50, n:1-2, pp:5-43 [Journal]
    
13. Christophe Andrieu, Nando de Freitas, Arnaud Doucet
    Robust Full Bayesian Learning for Radial Basis Networks. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2001, v:13, n:10, pp:2359-2407 [Journal]
    
14. João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee, Arnaud Doucet
    Sequential Monte Carlo Methods to Train Neural Network Models. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:4, pp:955-993 [Journal]
    
15. Christophe Andrieu, Arnaud Doucet
    Simulated annealing for maximum a Posteriori parameter estimation of hidden Markov models. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 2000, v:46, n:3, pp:994-1004 [Journal]
    
16. Sumeetpal S. Singh, Vladislav B. Tadic, Arnaud Doucet
    A policy gradient method for semi-Markov decision processes with application to call admission control. [Citation Graph (0, 0)][DBLP]
    European Journal of Operational Research, 2007, v:178, n:3, pp:808-818 [Journal]
    
17. Mike Klaas, Nando de Freitas, Arnaud Doucet
    Toward Practical N2 Monte Carlo: the Marginal Particle Filter. [Citation Graph (0, 0)][DBLP]
    UAI, 2005, pp:308-315 [Conf]
    
18. 
    Inference and Learning for Active Sensing, Experimental Design and Control. [Citation Graph (, )][DBLP]
    
    
19. 
    Sparse Bayesian nonparametric regression. [Citation Graph (, )][DBLP]
    
    
20. 
    Bayesian Policy Learning with Trans-Dimensional MCMC. [Citation Graph (, )][DBLP]
    
    
21. 
    Active Policy Learning for Robot Planning and Exploration under Uncertainty. [Citation Graph (, )][DBLP]
    
    
22. 
    A policy gradient method for SMDPs with application to call admission control. [Citation Graph (, )][DBLP]
    
    
23. 
    A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. [Citation Graph (, )][DBLP]
    
    
24. 
    A boosting approach to structure learning of graphs with and without prior knowledge. [Citation Graph (, )][DBLP]
    
    
25. 
    Channel Tracking for Relay Networks via Adaptive Particle MCMC [Citation Graph (, )][DBLP]