Christian A. Naesseth

Cited by

	All	Since 2016
Citations	479	449
h-index	9	9
i10-index	9	9

140

105

201420152016201720182019202020215 20 34 57 120 92 123 20

Co-authors

Fredrik LindstenAssociate Professor, Linköping UniversityVerified email at liu.se
Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Scott LindermanStanford UniversityVerified email at stanford.edu
Francisco J. R. RuizResearch Scientist at DeepmindVerified email at google.com
Rajesh RanganathAssistant Professor, NYUVerified email at cs.princeton.edu
Andreas Lindholm (Svensson)Machine Learning Research Engineer, AnnotellVerified email at annotell.com
Johan DahlinHead of Product, AgriOpt AB, Stockholm, SwedenVerified email at johandahlin.com
Tom RainforthUniversity of OxfordVerified email at stats.ox.ac.uk
Brooks PaigeAssociate Professor, University College LondonVerified email at ucl.ac.uk
Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca
Arnaud DoucetProfessor of Statistics, University of Oxford & Research Scientist, Google DeepMindVerified email at stats.ox.ac.uk
Adam M. JohansenUniversity of WarwickVerified email at warwick.ac.uk
John AstonStatistical Laboratory, DPMMS, University of CambridgeVerified email at statslab.cam.ac.uk
B KirkpatrickResearcher, Intrepid Net ComputingVerified email at intrepidnetcomputing.com
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
George TuckerGoogle BrainVerified email at google.com
Chris J. MaddisonUniversity of TorontoVerified email at cs.toronto.edu
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Dieterich LawsonStanford UniversityVerified email at stanford.edu

Christian A. Naesseth

Postdoctoral Research Scientist at Columbia University

Verified email at columbia.edu

Machine Learning Statistics Signal Processing System Identification


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Variational Sequential Monte Carlo CA Naesseth, SW Linderman, R Ranganath, DM Blei The 21st International Conference on Artificial Intelligence and Statistics …, 2018	116	2018
Reparameterization gradients through acceptance-rejection sampling algorithms CA Naesseth, FJR Ruiz, SW Linderman, DM Blei The 20th International Conference on Artificial Intelligence and Statistics …, 2017	76	2017
Nested Sequential Monte Carlo Methods CA Naesseth, F Lindsten, TB Schön The 32nd International Conference on Machine Learning (ICML) 37, 1292–1301, 2015	64	2015
Sequential Monte Carlo Methods for System Identification TB Schön, F Lindsten, J Dahlin, J Wågberg, CA Naesseth, A Svensson, ... IFAC Symposium on System Identification, 2015	62	2015
Sequential Monte Carlo for Graphical Models CA Naesseth, F Lindsten, TB Schön Advances in Neural Information Processing Systems 27, 2014	47	2014
Interacting Particle Markov Chain Monte Carlo T Rainforth, CA Naesseth, F Lindsten, B Paige, JW van de Meent, ... The 33rd International Conference on Machine Learning (ICML) 48, 2616–2625, 2016	28	2016
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	27	2017
Elements of Sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön Foundations and Trends® in Machine Learning 12 (3), 307-392, 2019	20	2019
High-dimensional filtering using nested sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön IEEE Transactions on Signal Processing 67 (16), 4177-4188, 2019	10	2019
Twisted Variational Sequential Monte Carlo D Lawson, G Tucker, CA Naesseth, CJ Maddison, RP Adams, YW Teh 3rd workshop on Bayesian Deep Learning (NeurIPS), 2018	9	2018
Capacity estimation of two-dimensional channels using Sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön The 2014 IEEE Information Theory Workshop, 2014	6	2014
Markovian Score Climbing: Variational Inference with KL(p\|\|q) CA Naesseth, F Lindsten, D Blei arXiv preprint arXiv:2003.10374, 2020	5	2020
Towards Automated Sequential Monte Carlo for Probabilistic Graphical Models CA Naesseth, F Lindsten, TB Schön NIPS Workshop on Black Box Inference and Learning, 2015	5	2015
Importance sampling with Hamiltonian dynamics CA Naesseth, F Lindsten NIPS 2015 workshop for scalable Monte Carlo methods, 2015	2	2015
Machine learning using approximate inference: Variational and sequential Monte Carlo methods CA Naesseth Linköping University Electronic Press, 2018	1	2018
Distributed, scalable and gossip-free consensus optimization with application to data analysis SK Pakazad, CA Naesseth, F Lindsten, A Hansson arXiv preprint arXiv:1705.02469, 2017	1	2017
Inverse articulated-body dynamics from video via variational sequential Monte Carlo D Biderman, CA Naesseth, L Wu, T Abe, AC Mosberger, LJ Sibener, ... First workshop on differentiable vision, graphics, and physics applied to …, 2020		2020

The system can't perform the operation now. Try again later.

Articles 1–17

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Upload PDF

Follow this author

Cited by

Co-authors