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
Jan-Willem van de MeentUniversity of Amsterdam and Northeastern UniversityVerified email at uva.nl
Grigory BartoshPhD candidate at University of AmsterdamVerified email at uva.nl
Francisco J. R. RuizResearch Scientist at DeepmindVerified email at google.com
John CunninghamProfessorVerified email at columbia.edu
Luhuan WuPhD student, Columbia UniversityVerified email at columbia.edu
Rajesh RanganathAssistant Professor, NYUVerified email at cs.princeton.edu
Antonio Khalil MorettiSpelman CollegeVerified email at cs.columbia.edu
Itsik Pe'erDepartment of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Brian L TrippeAssistant Professor of Statistics, Stanford UniversityVerified email at stanford.edu
Heiko ZimmermannUniversity 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
Johan WågbergPhD-student, Uppsala UniversityVerified email at it.uu.se
Dmitry VetrovProfessor of Computer Science at Constructor University, BremenVerified email at constructor.university
Liyi ZhangPrinceton UniversityVerified email at princeton.edu
Adam M. JohansenUniversity of WarwickVerified email at warwick.ac.uk

Christian A. Naesseth

Assistant Professor, University of Amsterdam

Verified email at uva.nl - Homepage

Machine Learning Statistics Generative Models Sciences


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Variational Sequential Monte Carlo CA Naesseth, SW Linderman, R Ranganath, DM Blei The 21st International Conference on Artificial Intelligence and Statistics …, 2018	270	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	136	2017
Elements of Sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön Foundations and Trends® in Machine Learning 12 (3), 307-392, 2019	121	2019
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	111	2015
Nested Sequential Monte Carlo Methods CA Naesseth, F Lindsten, TB Schön The 32nd International Conference on Machine Learning (ICML) 37, 1292–1301, 2015	91	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	72	2017
Practical and asymptotically exact conditional sampling in diffusion models L Wu, B Trippe, CA Naesseth, D Blei, JP Cunningham Advances in Neural Information Processing Systems 36, 2023	71	2023
Markovian Score Climbing: Variational Inference with KL(p\|\|q) CA Naesseth, F Lindsten, D Blei Advances in Neural Information Processing Systems 34, 2020	63	2020
Sequential Monte Carlo for Graphical Models CA Naesseth, F Lindsten, TB Schön Advances in Neural Information Processing Systems 27, 2014	53	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	43	2016
High-dimensional filtering using nested sequential Monte Carlo CA Naesseth, F Lindsten, TB Schön IEEE Transactions on Signal Processing 67 (16), 4177-4188, 2019	41	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	25	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	23	2021
A Variational Perspective on Generative Flow Networks H Zimmermann, F Lindsten, JW van de Meent, CA Naesseth Transactions on Machine Learning Research, 2023	22	2023
Variational Flow Matching for Graph Generation F Eijkelboom, G Bartosh, CA Naesseth, M Welling, JW van de Meent Advances in Neural Information Processing Systems 37, 2024	12	2024
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling G Bartosh, D Vetrov, CA Naesseth Advances in Neural Information Processing Systems 37, 2024	12	2024
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	9	2023
Equivariant Neural Diffusion for Molecule Generation FRJ Cornet, G Bartosh, MN Schmidt, CA Naesseth Advances in Neural Information Processing Systems 37, 2024	6	2024
Neural Diffusion Models G Bartosh, D Vetrov, CA Naesseth The 41st International Conference on Machine Learning (ICML) 235, 3073-3095, 2024	6	2024

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