Fredrik Lindsten

Background - Research - Publications - Invited talks

Position: Research Associate

E-mail: fsml2 [at] cam.ac.uk

Office Location: BN3-06

Background

Fredrik is a postdoctoral Research Associate at the Signal Processing and Communications Lab. He received his MSc degree in Applied Physics and Electrical Engineering in 2008, a Licentiate degree in Automatic Control in 2011, and a PhD in Automatic Control in 2013, all from Linköping University. His PhD was supervised by Prof. Thomas B. Schön and co-supervised by Prof. Lennart Ljung and Prof. Fredrik Gustafsson. In 2012, he spent the time from January to April at the University of California, Berkeley, as a Visiting Student Researcher with Prof. Michael I. Jordan.

Research Interests

Fredrik's research interests are in Monte Carlo methods for statistical inference in dynamical systems. He is interested in both state estimation and identification problems, primarily for nonlinear and/or non-Gaussian systems. He is working with various aspects of Monte Carlo, such as sequential Monte Carlo, Markov chain Monte Carlo, and the combination of the two in so called particle MCMC methods.

Publications

Recent preprints & working papers

S. S. Singh, F. Lindsten, and E. Moulines, "Blocking Strategies and Stability of Particle Gibbs Samplers". [arXiv]
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". [arXiv]
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

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 (accepted for publication). [arXiv]
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]
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]
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]
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]
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]
F. Lindsten, T. B. Schön, and M. I. Jordan, "Bayesian semiparametric Wiener system identification". Automatica, 49(7): 2053-2063, July 2013. [pdf] [Automatica]

Discussion contributions

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, (forthcoming).
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, (forthcoming).

Conference papers

2015

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; accepted for publication), Beijing, China, October 2015.
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; accepted for publication), Beijing, China, October 2015. [arXiv]
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; accepted for publication), Beijing, China, October 2015.
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; accepted for publication), Beijing, China, October 2015. [arXiv]
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]
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]
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.

2014

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]
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]
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.
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.
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.
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.
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)

2013

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]
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]
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]
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]
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]

2012

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]
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]
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]
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]
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]
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]

2009-2011

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]
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]
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]
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]
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]

Theses

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]
F. Lindsten, "Rao-Blackwellised particle methods for inference and identification". Licentiate's thesis LiU -TEK-LIC-2011:19, 2011. [pdf]

Invited talks

11/03/15: "Particle Gibbs with Ancestor Sampling", OxWaSP module, The University of Oxford, Oxford, UK. [slides]
26/02/15: "Nested Sequential Monte Carlo", Division of Automatic Control, Linköping University, Linköping, Sweden. [slides]
07/11/14: "Sequential Monte Carlo for graphical models: Graph decompositions and Divide-and-Conquer SMC", Probability and Statistics Seminars, School of Mathematics, University of Bristol, Bristol, UK. [slides]
31/10/14: "Sequential Monte Carlo for graphical models: Graph decompositions and Divide-and-Conquer SMC", Statistics Seminars, Imperial College London, London, UK.
23/10/14: "Sequential Monte Carlo for graphical models: Graph decompositions and Divide-and-Conquer SMC", Signal Processing Seminars, Department of Engineering, The University of Cambridge, Cambridge, UK.
06/01/14: "Particle Gibbs with Ancestor Sampling", MCMSki IV, Chamonix, France. [slides]
29/11/13: "Inference in nonlinear state-space models using Particle Gibbs with Ancestor Sampling", Centre for Mathematical Sciences, Lund University, Lund, Sweden. [slides]
01/11/13: "Conditional particle filters for system identification", Division of Automatic Control, Linköping University, Linköping, Sweden. [slides]
08/10/12: "Ancestor Sampling for Particle Gibbs", INRIA, Paris, France.
16/06/11: "Rao-Blackwellised particle methods for inference and identification", Department of Signals and systems, Chalmers University of Technology, Gothenburg, Sweden. [slides]

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