Christian A. Naesseth

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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, KognicVerified email at kognic.com
Johan DahlinKotte Consulting AB, Stockholm, SwedenVerified email at johandahlin.com
Johan WågbergPhD-student, Uppsala UniversityVerified email at it.uu.se
Adam M. JohansenUniversity of WarwickVerified email at warwick.ac.uk
John AstonStatistical Laboratory, DPMMS, University of CambridgeVerified email at statslab.cam.ac.uk
B KirkpatrickIntrepid Net ComputingVerified email at intrepidnetcomputing.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
Liyi ZhangColumbia UnviersityVerified email at columbia.edu
Antonio Khalil MorettiBarnard College and Columbia UniversityVerified email at cs.columbia.edu
Itsik Pe'erDepartment of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu

Christian A. Naesseth

Assistant Professor at University of Amsterdam

Verified email at uva.nl - Homepage

Machine Learning Statistics Generative Models Causality Sciences


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	225	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	122	2017
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	103	2015
Nested Sequential Monte Carlo Methods CA Naesseth, F Lindsten, TB Schön The 32nd International Conference on Machine Learning (ICML) 37, 1292–1301, 2015	89	2015
Elements of Sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön Foundations and Trends® in Machine Learning 12 (3), 307-392, 2019	83	2019
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	52	2017
Sequential Monte Carlo for Graphical Models CA Naesseth, F Lindsten, TB Schön Advances in Neural Information Processing Systems 27, 2014	51	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	40	2016
Markovian Score Climbing: Variational Inference with KL(p\|\|q) CA Naesseth, F Lindsten, D Blei Advances in Neural Information Processing Systems 34, 2020	37	2020
High-dimensional filtering using nested sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön IEEE Transactions on Signal Processing 67 (16), 4177-4188, 2019	26	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	19	2018
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference AK Moretti, L Zhang, CA Naesseth, H Venner, D Blei, I Pe'er The 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021	13	2021
A Variational Perspective on Generative Flow Networks H Zimmermann, F Lindsten, JW van de Meent, CA Naesseth Transactions on Machine Learning Research, 2023	6	2023
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
E-valuating classifier two-sample tests T Pandeva, T Bakker, CA Naesseth, P Forré arXiv preprint arXiv:2210.13027, 2022	5	2022
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport L Zhang, DM Blei, CA Naesseth Transactions on Machine Learning Research, 2023	4	2023
Towards Automated Sequential Monte Carlo for Probabilistic Graphical Models CA Naesseth, F Lindsten, TB Schön NIPS Workshop on Black Box Inference and Learning, 2015	4	2015
Practical and asymptotically exact conditional sampling in diffusion models L Wu, BL Trippe, CA Naesseth, DM Blei, JP Cunningham arXiv preprint arXiv:2306.17775, 2023	2	2023
Machine learning using approximate inference: Variational and sequential Monte Carlo methods CA Naesseth Linköping University Electronic Press, 2018	2	2018
Importance sampling with Hamiltonian dynamics CA Naesseth, F Lindsten NIPS 2015 workshop for scalable Monte Carlo methods, 2015	2	2015

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