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Christian A. Naesseth
Christian A. Naesseth
Assistant Professor at University of Amsterdam
Verifierad e-postadress på uva.nl
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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
1732018
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
1022017
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
792015
Nested Sequential Monte Carlo Methods
CA Naesseth, F Lindsten, TB Schön
The 32nd International Conference on Machine Learning (ICML) 37, 1292–1301, 2015
762015
Elements of Sequential Monte Carlo
CA Naesseth, F Lindsten, TB Schön
Foundations and Trends® in Machine Learning 12 (3), 307-392, 2019
432019
Sequential Monte Carlo for Graphical Models
CA Naesseth, F Lindsten, TB Schön
Advances in Neural Information Processing Systems 27, 2014
432014
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
392017
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
352016
Markovian Score Climbing: Variational Inference with KL(p||q)
CA Naesseth, F Lindsten, D Blei
Advances in Neural Information Processing Systems 34, 2020
232020
High-dimensional filtering using nested sequential Monte Carlo
CA Naesseth, F Lindsten, TB Schön
IEEE Transactions on Signal Processing 67 (16), 4177-4188, 2019
192019
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
152018
Capacity estimation of two-dimensional channels using Sequential Monte Carlo
CA Naesseth, F Lindsten, TB Schön
The 2014 IEEE Information Theory Workshop, 2014
62014
Towards Automated Sequential Monte Carlo for Probabilistic Graphical Models
CA Naesseth, F Lindsten, TB Schön
NIPS Workshop on Black Box Inference and Learning, 2015
42015
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
32021
Importance sampling with Hamiltonian dynamics
CA Naesseth, F Lindsten
NIPS 2015 workshop for scalable Monte Carlo methods, 2015
22015
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport
L Zhang, CA Naesseth, DM Blei
arXiv preprint arXiv:2202.01841, 2022
12022
Machine learning using approximate inference: Variational and sequential Monte Carlo methods
CA Naesseth
Linköping University Electronic Press, 2018
12018
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
12017
Robust Gaussian process regression with G-confluent likelihood
M Lindfors, T Chen, CA Naesseth
IFAC-PapersOnLine 53 (2), 394-399, 2020
2020
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
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