Fredrik Lindsten

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Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
Christian A. NaessethAssistant Professor, University of AmsterdamVerified email at uva.nl
Andreas Lindholm (Svensson)Machine Learning Research Engineer, KognicVerified email at kognic.com
Johan DahlinKotte Consulting 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
David Müller-WidmannProduct engineer, Pumas-AI Inc.Verified email at pumas.ai
Jacob RollVision Predevelopment, VeoneerVerified email at veoneer.com
Niklas WahlströmAssociate Professor, Uppsala University, SwedenVerified email at it.uu.se
Lennart SvenssonProfessor in Signal Processing, Chalmers University of TechnologyVerified email at chalmers.se
Henrik OhlssonChief and VP of Data Science, C3 AI/Visiting Prof., UC Berkeley/Assistant Prof., LinköpingVerified email at isy.liu.se
Fredrik GustafssonProf, Linköping University, SwedenVerified email at liu.se
Arnaud DoucetGoogle DeepMindVerified email at google.com
Lawrence MurrayUber AIVerified email at uber.com
Pete BunchCambridge UniversityVerified email at cam.ac.uk
Lennart LjungProfessor of Automatic Control, Linköping University, SwedenVerified email at isy.liu.se
Dave ZachariahAssociate Professor, Uppsala UniversityVerified email at it.uu.se
Johan WågbergPhD-student, Uppsala UniversityVerified email at it.uu.se
Anna WigrenPhD student, Uppsala UniversityVerified email at it.uu.se
Adrian WillsUniversity of Newcastle AustraliaVerified email at newcastle.edu.au
Joel OskarssonLinköping UniversityVerified email at liu.se

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 The Journal of Machine Learning Research 15 (1), 2145-2184, 2014	351	2014
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	266	2019
Machine learning: a first course for engineers and scientists A Lindholm, N Wahlström, F Lindsten, TB Schön Cambridge University Press, 2022	244	2022
Backward simulation methods for Monte Carlo statistical inference F Lindsten, TB Schön Foundations and Trends® in Machine Learning 6 (1), 1-143, 2013	203	2013
Bayesian inference and learning in Gaussian process state-space models with particle MCMC R Frigola, F Lindsten, TB Schön, CE Rasmussen Advances in neural information processing systems 26, 2013	181	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	149	2011
Just relax and come clustering!: A convexification of k-means clustering F Lindsten, H Ohlsson, L Ljung Linköping University Electronic Press, 2011	121	2011
Calibration tests in multi-class classification: A unifying framework D Widmann, F Lindsten, D Zachariah Advances in neural information processing systems 32, 2019	120	2019
Elements of sequential monte carlo CA Naesseth, F Lindsten, TB Schön Foundations and Trends® in Machine Learning 12 (3), 307-392, 2019	119	2019
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	109	2015
Nested sequential monte carlo methods C Naesseth, F Lindsten, T Schon International Conference on Machine Learning, 1292-1301, 2015	91	2015
Sequential kernel herding: Frank-Wolfe optimization for particle filtering S Lacoste-Julien, F Lindsten, F Bach Artificial Intelligence and Statistics, 544-552, 2015	91	2015
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), 2571, 2018	88	2018
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	82	2014
Divide-and-conquer with sequential Monte Carlo F Lindsten, AM Johansen, CA Naesseth, B Kirkpatrick, TB Schön, ... Journal of Computational and Graphical Statistics 26 (2), 445-458, 2017	72	2017
An efficient stochastic approximation EM algorithm using conditional particle filters F Lindsten 2013 IEEE international conference on acoustics, speech and signal …, 2013	72	2013
Ancestor sampling for particle Gibbs F Lindsten, T Schön, M Jordan Advances in Neural Information Processing Systems 25, 2012	69	2012
Smoothing with couplings of conditional particle filters PE Jacob, F Lindsten, TB Schön Journal of the American Statistical Association, 2020	68	2020
Uniform ergodicity of the particle Gibbs sampler F Lindsten, R Douc, E Moulines Scandinavian Journal of Statistics 42 (3), 775-797, 2015	68	2015
Bayesian semiparametric Wiener system identification F Lindsten, TB Schön, MI Jordan Automatica 49 (7), 2053-2063, 2013	65	2013

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