Sequential kernel herding: Frank-Wolfe optimization for particle filtering

Journal of Machine Learning Research

Volumn 38, Issue , 2015, Pages 544-552

  Lacoste Julien, Simon ^a   Lindsten, Fredrik ^b   Bach, Francis ^a  

^a ECOLE NORMALE SUPÉRIEURE   (France)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; OPTIMIZATION; ROBOT APPLICATIONS; SIGNAL FILTERING AND PREDICTION;

ADAPTIVE QUADRATURE; COMPUTATIONAL COSTS; EMISSION PROBABILITIES; MONTE CARLO INTEGRATION; OPTIMIZATION ALGORITHMS; PARTICLE FILTERING; QUASI-MONTE CARLO SAMPLINGS; REPRODUCING KERNEL HILBERT SPACES;

MONTE CARLO METHODS;

EID: 84954318104     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (23)

1. F. Bach. Learning with submodular functions: A convex optimization perspective. Foundations and Trends in Machine Learning, 6(2-3):145-373, 2013.
2. F. Bach, S. Lacoste-Julien, and G. Obozinski. On the equivalence between herding and conditional gradient algorithms. In Proceedings of the 29th International Conference on Machine Learning (ICML), pages 1359-1366, 2012.
3. O. Cappe, E. Moulines, and T. Ryden. Inference in Hidden Markov Models. Springer, 2005.
4. J. Carpenter, P. Clifford, and P. Fearnhead. Improved particle filter for nonlinear problems. IEE Proceedings Radar, Sonar and Navigation, 146(1):2-7, 1999.
5. Y. Chen, M. Welling, and A. Smola. Super-samples from kernel herding. In Proceedings of the 26th International Conference on Machine Learning (ICML), 2010.
6. R. Douc, E. Moulines, and J. Olsson. Long-term stability of sequential Monte Carlo methods under verifiable conditions. Annals of Applied Probability, 24 (5):1767-1802, 2014.
7. A. Doucet and A. Johansen. A tutorial on particle filtering and smoothing: Fifteen years later. In D. Crisan and B. Rozovsky, editors, The Oxford Handbook of Nonlinear Filtering. Oxford University Press, 2011.
8. A. Doucet, S. J. Godsill, and C. Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3):197-208, 2000.
9. J. C. Dunn. Convergence rates for conditional gradient sequences generated by implicit step length rules. SIAM Journal on Control and Optimization, 18:473-487, 1980.
10. P. Fearnhead. Using random quasi-Monte-Carlo within particle filters, with application to financial time series. Journal ofComputational and Graphical Statistics, 14(4):751-769, 2005.
11. M. Frank and P. Wolfe. An algorithm for quadratic programming. Naval Research Logistics Quarterly, 3:95-110, 1956.
12. M. Gerber and N. Chopin. Sequential quasi-Monte Carlo. arXiv preprint arXiv:1402.4039v5, 2014.
13. N. J. Gordon, D. J. Salmond, and A. F. M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing, IEE Proceedings F, 140(2):107-113, Apr. 1993.
14. A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Scholkopf, and A. Smola. A kernel two-sample test. The Journal of Machine Learning Research, 13:723-773, 2012.
15. F. Huszar and D. Duvenaud. Optimally-weighted herding is Bayesian quadrature. In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI), pages 377-385, 2012.
16. M. Jaggi. Revisiting Frank-Wolfe: Projection-free sparse convex optimization. In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013.
17. D. Ormoneit, C. Lemieux, and D. J. Fleet. Lattice particle filters. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (UAI), pages 395-402, 2001.
18. V. Philomin, R. Duraiswami, and L. Davis. Quasi-random sampling for condensation. In Proceedings of the 6th European Conference on Computer Vision (ECCV), 2000.
19. M. K. Pitt and N. Shephard. Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association, 94(446):590-599, 1999.
20. B. Ristic, S. Arulampalam, and N. Gordon. Beyond the Kalman filter: particle filters for tracking applications. Artech House, London, UK, 2004.
21. A. Smola, A. Gretton, L. Song, and B. Schoolkopf. A Hilbert space embedding for distributions. In Algorithmic Learning Theory, pages 13-31. Springer, 2007.
22. B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schoolkopf, and G. R. Lanckriet. Hilbert space em-beddings and metrics on probability measures. The Journal of Machine Learning Research, 99:1517-1561, 2010.
23. D. Toornqvist, T. B. Schoon, R. Karlsson, and F. Gustafsson. Particle filter SLAM with high dimensional vehicle model. Journal of Intelligent and Robotic Systems, 55(4):249-266, 2009.