Fredrik Lindsten

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	All	Since 2016
Citations	2064	1525
h-index	26	22
i10-index	54	44

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Co-authors

Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
Christian A. NaessethPostdoctoral Research Scientist at Columbia UniversityVerified email at columbia.edu
Johan DahlinHead of Product, AgriOpt AB, Stockholm, SwedenVerified email at johandahlin.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Andreas Lindholm (Svensson)Machine Learning Research Engineer, AnnotellVerified email at annotell.com
Henrik OhlssonVP of Data Science, C3.ai/Visiting Prof., UC Berkeley/Assistant Prof., Linköping UniversityVerified email at isy.liu.se
Fredrik GustafssonProf, Linköping University, SwedenVerified email at liu.se
Pete BunchCambridge UniversityVerified email at cam.ac.uk
Arnaud DoucetProfessor of Statistics, University of Oxford & Research Scientist, Google DeepMindVerified email at stats.ox.ac.uk
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Roger FrigolaMachine Learning at www.rogerfrigola.comVerified email at rogerfrigola.com
Lennart LjungProfessor of Automatic Control, Linköping University, SwedenVerified email at isy.liu.se
Lennart SvenssonProfessor in Signal Processing, Chalmers University of TechnologyVerified email at chalmers.se
Lawrence MurrayUber AIVerified email at uber.com
Simon GodsillUniversity of CambridgeVerified email at eng.cam.ac.uk
David WidmannPhD student, Uppsala UniversityVerified email at it.uu.se
Adrian WillsUniversity of Newcastle AustraliaVerified email at newcastle.edu.au
Eric MoulinesProfesseur, Ecole Polytechnique, Membre de l'Académie des SciencesVerified email at polytechnique.edu
Sumeetpal Sidhu SinghReader in Engineering Statistics, Department of Engineering, Cambridge UniversityVerified email at cam.ac.uk
Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca

Fredrik Lindsten

Associate Professor, Linköping University

Verified email at liu.se - Homepage

Computational Statistics Machine Learning Monte Carlo Methods System Identification


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Particle Gibbs with Ancestor Sampling F Lindsten, MI Jordan, TB Schön Journal of Machine Learning Research 15, 2145-2184, 2014	224	2014
Backward simulation methods for Monte Carlo statistical inference F Lindsten, TB Schön Foundations and Trends® in Machine Learning 6 (1), 1-143, 2013	144	2013
Bayesian inference and learning in Gaussian process state-space models with particle MCMC R Frigola, F Lindsten, TB Schön, CE Rasmussen arXiv preprint arXiv:1306.2861, 2013	106	2013
Clustering using sum-of-norms regularization: With application to particle filter output computation F Lindsten, H Ohlsson, L Ljung 2011 IEEE Statistical Signal Processing Workshop (SSP), 201-204, 2011	68	2011
Nested sequential Monte Carlo methods CA Naesseth, F Lindsten, TB Schön 32nd International Conference on Machine Learning (ICML), 2015	64	2015
Sequential Monte Carlo methods for system identification TB Schön, F Lindsten, J Dahlin, J Wågberg, CA Naesseth, A Svensson, ... IFAC-PapersOnLine 48 (28), 775-786, 2015	62	2015
Just relax and come clustering!: A convexification of k-means clustering F Lindsten, H Ohlsson, L Ljung Linköping University Electronic Press, 2011	58	2011
Ancestor sampling for particle Gibbs F Lindsten, MI Jordan, TB Schön Advances in Neural Information Processing Systems 25, 2600-2608, 2012	56	2012
Recursive maximum likelihood identification of jump Markov nonlinear systems E Özkan, F Lindsten, C Fritsche, F Gustafsson IEEE Transactions on Signal Processing 63 (3), 754-765, 2014	53	2014
An efficient stochastic approximation EM algorithm using conditional particle filters F Lindsten 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013	53	2013
Bayesian semiparametric Wiener system identification F Lindsten, TB Schön, MI Jordan Automatica 49 (7), 2053-2063, 2013	52	2013
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering S Lacoste-Julien, F Lindsten, F Bach 18th International Conference on Artificial Intelligence and Statistics …, 2015	51	2015
Sequential Monte Carlo for graphical models CA Naesseth, F Lindsten, TB Schön arXiv preprint arXiv:1402.0330, 2014	47	2014
Uniform ergodicity of the Particle Gibbs sampler F Lindsten, R Douc, E Moulines Scandinavian Journal of Statistics 42 (3), 775-797, 2015	45	2015
Rao-Blackwellised particle methods for inference and identification F Lindsten Linköping University Electronic Press, 2011	45	2011
Particle Metropolis–Hastings using gradient and Hessian information J Dahlin, F Lindsten, TB Schön Statistics and computing 25 (1), 81-92, 2015	44	2015
Geo-referencing for UAV navigation using environmental classification F Lindsten, J Callmer, H Ohlsson, D Törnqvist, TB Schön, F Gustafsson 2010 IEEE International Conference on Robotics and Automation, 1420-1425, 2010	43	2010
Coherent modulation of the sea-level annual cycle in the United States by Atlantic Rossby waves FM Calafat, T Wahl, F Lindsten, J Williams, E Frajka-Williams Nature communications 9 (1), 1-13, 2018	42	2018
On the use of backward simulation in the particle Gibbs sampler F Lindsten, TB Schön 37th IEEE International Conference on Acoustics, Speech, and Signal …, 2012	37	2012
Evaluating model calibration in classification J Vaicenavicius, D Widmann, C Andersson, F Lindsten, J Roll, T Schön The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	33	2019

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