Christian A. Naesseth

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Fredrik LindstenAssociate Professor, Linköping UniversityVerified email at liu.se
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
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
Antonio Khalil MorettiSpelman CollegeVerified email at cs.columbia.edu
Itsik Pe'erDepartment of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Adam M. JohansenUniversity of WarwickVerified email at warwick.ac.uk
John AstonStatistical Laboratory, DPMMS, University of CambridgeVerified email at statslab.cam.ac.uk
B KirkpatrickBowtie ComputingVerified email at bowtiecomputing.com
Heiko ZimmermannUniversity of AmsterdamVerified email at uva.nl
John CunninghamProfessor, Columbia UniversityVerified email at columbia.edu
Luhuan WuPhD student, Columbia UniversityVerified email at columbia.edu
Liyi ZhangPrinceton UniversityVerified email at princeton.edu
Jan-Willem van de MeentUniversity of Amsterdam and Northeastern UniversityVerified email at uva.nl
Tom RainforthUniversity of OxfordVerified email at stats.ox.ac.uk

Christian A. Naesseth

Assistant Professor, University of Amsterdam

Verified email at uva.nl - Homepage

Machine Learning Statistics Generative Models 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	254	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	132	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	105	2015
Elements of Sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön Foundations and Trends® in Machine Learning 12 (3), 307-392, 2019	104	2019
Nested Sequential Monte Carlo Methods CA Naesseth, F Lindsten, TB Schön The 32nd International Conference on Machine Learning (ICML) 37, 1292–1301, 2015	90	2015
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	74	2017
Markovian Score Climbing: Variational Inference with KL(p\|\|q) CA Naesseth, F Lindsten, D Blei Advances in Neural Information Processing Systems 34, 2020	56	2020
Sequential Monte Carlo for Graphical Models CA Naesseth, F Lindsten, TB Schön Advances in Neural Information Processing Systems 27, 2014	52	2014
High-dimensional filtering using nested sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön IEEE Transactions on Signal Processing 67 (16), 4177-4188, 2019	44	2019
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	41	2016
Practical and asymptotically exact conditional sampling in diffusion models L Wu, B Trippe, C Naesseth, D Blei, JP Cunningham Advances in Neural Information Processing Systems 36, 2023	27	2023
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	23	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	18	2021
A Variational Perspective on Generative Flow Networks H Zimmermann, F Lindsten, JW van de Meent, CA Naesseth Transactions on Machine Learning Research, 2023	16	2023
E-valuating classifier two-sample tests T Pandeva, T Bakker, CA Naesseth, P Forré Transactions on Machine Learning Research, 2024	11	2024
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport L Zhang, DM Blei, 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
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
Neural Diffusion Models G Bartosh, D Vetrov, CA Naesseth The 41st International Conference on Machine Learning (ICML) 235, 3073-3095, 2024	3	2024
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	2	2020

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