Department of Information Technology

Publications

See also my Google Scholar page.

Recent preprints & working papers
4. R. S. Risuleo, F. Lindsten, and H. Hjalmarsson. Nonparametric kernel-based estimation of Wiener systems.
3. C. A. Naesseth, F. Lindsten, and T. B. Schön. High-dimensional Filtering using Nested Sequential Monte Carlo. arXiv
2. F. Lindsten and A. Doucet. Pseudo-Marginal Hamiltonian Monte Carlo. arXiv
1. F. Lindsten, P. Bunch, S. S. Singh, and T. B. Schön. Particle ancestor sampling for near-degenerate or intractable state transition models. arXiv
Journal papers
13. P. E. Jacob, F. Lindsten, and T. B. Schön. Smoothing with Couplings of Conditional Particle Filters. Journal of the American Statistical Association (forthcoming) arXiv
12. F. M. Calafat, T. Wahl, F. Lindsten, J. Williams, and E. Frajka-Williams. Coherent modulation of the sea-level annual cycle in the United States by Atlantic Rossby waves. Nature Communications, 9(2571), 2018. Nature
11. T. B. Schön, A. Svensson, L. Murray, and F. Lindsten. Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. Mechanical Systems and Signal Processing, 104:866-883, 2018. arXiv
10. A. Svensson, T. B. Schön, and F. Lindsten. Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution. Mechanical Systems and Signal Processing, 104:915-928, 2018. arXiv
9. S. S. Singh, F. Lindsten, and E. Moulines. Blocking strategies and stability of particle Gibbs samplers. Biometrika, 104(4):953–969, 2017. OUP arXiv
8. F. Lindsten, A. M. Johansen, C. A. Naesseth, B. Kirkpatrick, T. B. Schön, J. Aston, and A. Bouchard-Côté. Divide-and-Conquer with Sequential Monte Carlo. Journal of Computational and Graphical Statistics, 26(2):445-458, 2017. ASA arXiv
7. F. Lindsten, P. Bunch, S. Särkkä, T. B. Schön, and S. J. Godsill. Rao-Blackwellized particle smoothers for conditionally linear Gaussian models. IEEE Journal of Selected Topics in Signal Processing, 10(2):353-365, 2016. arXiv
6. F. Lindsten, R. Douc, and E. Moulines. Uniform ergodicity of the Particle Gibbs sampler. Scandinavian Journal of Statistics, 42(3): 775-797, 2015. Wiley arXiv
5. E. Özkan, F. Lindsten, C. Fritsche, and F. Gustafsson. Recursive maximum likelihood identification of jump Markov nonlinear systems. IEEE Transactions on Signal Processing, 63(3): 754-765, February 2015. IEEE
4. J. Dahlin, F. Lindsten, and T. B. Schön. Particle Metropolis-Hastings using gradient and Hessian information. Statistics and Computing, 25(1): 81-92, 2015. arXiv S&C
3. F. Lindsten, M. I. Jordan, and T. B. Schön. Particle Gibbs with Ancestor Sampling. Journal of Machine Learning Research, 15: 2145-2184, June 2014. pdf JMLR
2. F. Lindsten and T. B. Schön. Backward simulation methods for Monte Carlo statistical inference. Foundations and Trends in Machine Learning, 6(1): 1-143, August 2013. pdf now
1. F. Lindsten, T. B. Schön, and M. I. Jordan. Bayesian semiparametric Wiener system identification. Automatica, 49(7): 2053-2063, July 2013. pdfAutomatica
Discussion contributions
2. S. Lacoste-Julien and F. Lindsten, Discussion on "Sequential Quasi-Monte-Carlo Sampling" by Gerber and Chopin. Journal of the Royal Statistical Society: Series B, 77(3): 564-565, June 2015.
1. F. Lindsten and S. S. Singh, Discussion on "Sequential Quasi-Monte-Carlo Sampling" by Gerber and Chopin. Journal of the Royal Statistical Society: Series B, 77(3): 566-567, June 2015.
Peer-reviewed conference papers
34. R. S. Risuleo, F. Lindsten, and H. Hjalmarsson. Semi-parametric kernel-based identification of Wiener systems. To be presented at the 57th IEEE Conference on Decision and Control (CDC), Miami Beach, FL, USA, December 2018.
33. A. Wigren, L. Murray, and F. Lindsten. Improving the particle filter in high dimensions using conjugate artificial process noise. Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July 2018. arXiv
32. A. Svensson, F. Lindsten, and T. B. Schön. Learning Nonlinear State-Space Models Using Smooth Particle-Filter-Based Likelihood Approximations. Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July 2018. arXiv
31. T. Rainforth, C. A. Naesseth, F. Lindsten, B. Paige, J.-W. van de Meent, A. Doucet, and F. Wood. Interacting Particle Markov Chain Monte Carlo, Proceedings of the 33rd International Conference on Machine Learning (ICML), New York, USA, June 2016. arXiv
30. J. Wågberg, F. Lindsten, and T. B. Schön. Bayesian nonparametric identification of piecewise affine ARX systems, Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015.
29. J. Dahlin, F. Lindsten, and T. B. Schön. Quasi-Newton particle Metropolis-Hastings applied to intractable likelihood models, Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015. arXiv
28. M. Riabiz, F. Lindsten, and S. J. Godsill. Pseudo-Marginal MCMC for Parameter Estimation in Alpha-Stable Distributions, Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015.
27. T. B. Schön, F. Lindsten, J. Dahlin, J. Wågberg, C. A. Naesseth, A. Svensson, and L. Dai. Sequential Monte Carlo Methods for System Identification, Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015. arXiv
26. C. A. Naesseth, F. Lindsten, and T. B. Schön. Nested Sequential Monte Carlo Methods. Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France 2015. arXiv
25. S. Lacoste-Julien, F. Lindsten, and F. Bach. Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, USA, May 2015. arXiv
24. P. Bunch, F. Lindsten, and S. S. Singh. Particle Gibbs with refreshed backward simulation. Proceeding of the 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, April 2015.
23. C. A. Naesseth, F. Lindsten and T. B. Schön. Sequential Monte Carlo for Graphical Models. Advances in Neural Information Processing Systems (NIPS) 27, 1862-1870, 2014. pdf
22. C. A. Naesseth, F. Lindsten and T. B. Schön. Capacity estimation of two-dimensional channels using Sequential Monte Carlo. Proceedings of the 2014 IEEE Information Theory Workshop (ITW), Hobart, Tasmania, November 2014. arXiv
21. A. Svensson, T. B. Schön, and F. Lindsten. Identification of jump Markov linear models using particle filters. Proceedings of the 53rd IEEE Conference on Decision and Control (CDC), Los Angeles, USA, December 2014.
20. R. Frigola, F. Lindsten, T. B. Schön, and C. E. Rasmussen. Identification of Gaussian process state-space models with particle stochastic approximation EM. Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, August 2014.
19. J. Dahlin and F. Lindsten. Particle filter-based Gaussian process optimisation for parameter inference. Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, August 2014.
18. J. Dahlin, F. Lindsten, and T. B. Schön. Second-order particle MCMC for Bayesian parameter inference. Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, August 2014.
17. F. Gunnarsson, F. Lindsten, and N. Carlsson. Particle Filtering for Network-Based Positioning Terrestrial Radio Networks. IET Conference on Data Fusion and Target Tracking, Liverpool, UK, 2014. (ISIF Best Paper Award)
16. R. Frigola, F. Lindsten, T. B. Schön, and C. E. Rasmussen. Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. Advances in Neural Information Processing Systems (NIPS) 26, 3156-3164, 2013. pdf
15. F. Lindsten. An efficient stochastic approximation EM algorithm using conditional particle filters. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. pdf
14. J. Dahlin, F. Lindsten, and T. B. Schön. Particle Metropolis Hastings using Langevin dynamics. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. pdf
13. F. Lindsten, P. Bunch, S. J. Godsill, and T. B. Schön. Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. pdf
12. E. Taghavi, F. Lindsten, L. Svensson, and T. B. Schön. Adaptive stopping for fast particle smoothing. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. pdf
11. F. Lindsten, M. I. Jordan, and T. B. Schön. Ancestor Sampling for Particle Gibbs. Advances in Neural Information Processing Systems (NIPS) 25, 2600-2608, 2012. pdf
10. F. Lindsten, T. B. Schön, and M. I. Jordan. A semiparametric Bayesian approach to Wiener system identification. Proceedings of the 16th IFAC Symposium on System Identification, Brussels, Belgium, 2012. pdf [code]
9. F. Lindsten, T. B. Schön, and L. Svensson. A non-degenerate Rao-Blackwellised particle filter for estimating static parameters in dynamical models. Proceedings of the 16th IFAC Symposium on System Identification, Brussels, Belgium, 2012. pdf
8. J. Dahlin, F. Lindsten, T. B. Schön, and Adrian Wills. Hierarchical Bayesian ARX models for robust inference. Proceedings of the 16th IFAC Symposium on System Identification, Brussels, Belgium, 2012. pdf
7. A. Wills, T. B. Schön, F. Lindsten, and B. Ninness. Estimation of Linear Systems using a Gibbs Sampler. Proceedings of the 16th IFAC Symposium on System Identification, Brussels, Belgium, 2012. pdf
6. F. Lindsten and T. B. Schön. On the use of backward simulation in the particle Gibbs sampler. Proceedings of the 37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, 2012. pdf
5. F. Lindsten, H. Ohlsson, and L. Ljung. Clustering using sum-of-norms regularization; with application to particle filter output computation. Proceedings of the 2011 IEEE Workshop on Statistical Signal Processing (SSP), Nice, France, 2011. pdf
4. F. Lindsten, T. B. Schön, and J. Olsson. An explicit variance reduction expression for the Rao-Blackwellised particle filter. Proceedings of the 18th World Congress of the International Federation of Automatic Control (IFAC), Milan, Italy, 2011. pdf
3. F. Lindsten and T. B. Schön. Identification of Mixed Linear/Nonlinear State-Space Models. Proceedings of the 49th IEEE Conference on Decision and Control (CDC), Atlanta, USA, 2010. pdf
2. F. Lindsten, J. Callmer, H. Ohlsson, D. Törnqvist, T. B. Schön, and F. Gustafsson. Geo-referencing for UAV Navigation using Environmental Classification. Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, USA, 2010. pdf
1. F. Lindsten, P.J. Nordlund, and F. Gustafsson. Conflict Detection Metrics for Aircraft Sense and Avoid Systems. Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SafeProcess), Barcelona, Spain, 2009. pdf
Other publications
1. C. A. Naesseth, F. Lindsten, and T. B. Schön. Towards automated sequential Monte Carlo for probabilistic graphical models. In NIPS Workshop on Black Box Inference and Learning, 2015.
Theses
2. F. Lindsten. Particle Filters and Markov Chains for Learning of Dynamical Systems. PhD thesis, Linköping Studies in Science and Technology. Dissertations, No. 1530, 2013. pdf
1. F. Lindsten. Rao-Blackwellised particle methods for inference and identification. Licentiate's thesis. LiU -TEK-LIC-2011:19, 2011. pdf
Updated  2018-07-25 19:11:32 by Fredrik Lindsten.