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Publications

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(Pre-print PDF) (arXiv) (Bib) (Code/Data) (Link to published article)

Recent pre-prints

[J*]
C. A. Naesseth, F. Lindsten, T. B. Schön, High-dimensional Filtering using Nested Sequential Monte Carlo. arXiv.org, arXiv:1612.09162, 2016.

Refereed papers

[W2]
D. Lawson, G. Tucker, C. A. Naesseth, C. J. Maddison, R. P. Adams, Y. W. Teh, Twisted Variational Sequential Monte Carlo. Bayesian Deep Learning (NeurIPS Workshop), Montreal, Canada, December 2018.
[C7]
C. A. Naesseth, S. W. Linderman, R. Ranganath, D. M. Blei, Variational Sequential Monte Carlo. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Lanzarote, Spain, April 2018. (accepted for publication)
[C6]
C. A. Naesseth, F. J. R. Ruiz, S. Linderman, D. M. Blei, Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, USA, April 2017. (Best Paper Award)
[J1]
F. Lindsten, A. M. Johansen, C. A. Naesseth, B. Kirkpatrick, T. B. Schön, J. Aston and A. Bouchard-Côté, Divide-and-Conquer with Sequential Monte Carlo. Journal of Computational and Graphical Statistics. 2016.
[C5]
T. Rainforth*, C. A. Naesseth*, F. Lindsten, B. Paige, J-W. van de Meent, A. Doucet, F. Wood, Interacting Particle Markov Chain Monte Carlo. Proceedings of the 33rd International Conference on Machine Learning (ICML), New York, USA, June 2016. * equal contribution
[C4]
T. B. Schön, F. Lindsten, J. Dahlin, J. Wågberg, C. A. Naesseth, A. Svensson and L. Dai, Sequential Monte Carlo Methods for System Identification. Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015.
[C3]
C. A. Naesseth, F. Lindsten and T. B. Schön, Nested Sequential Monte Carlo Methods. Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, July 2015.
[W1]
C. A. Naesseth, F. Lindsten and T. B. Schön, Towards Automated Sequential Monte Carlo for Probabilistic Graphical Models. Black Box Inference and Learning NIPS Workshop, Montreal, Canada, December 2015.
[C2]
C. A. Naesseth, F. Lindsten and T. B. Schön, Sequential Monte Carlo for Graphical Models. Advances in Neural Information Processing Systems (NIPS) 27, Montreal, Canada, December 2014.
[C1]
C. A. Naesseth, F. Lindsten and T. B. Schön, Capacity estimation of two-dimensional channels using Sequential Monte Carlo. Proceedings of the 2014 IEEE Information Theory Workshop (ITW), Hobart, Australia, November 2014.

Other papers

[O1]
S. Khoshfetrat Pakazad, C. A. Naesseth, F. Lindsten and A. Hansson, Distributed, scalable and gossip-free consensus optimization with application to data analysis. arXiv.org, arXiv:1705.02469 , 2017.

Theses

[T2]
C. A. Naesseth, Vision and Radar Sensor Fusion for Advanced Driver Assistance Systems. Master's thesis, LiTH-ISY-EX-13/4685, 2013.
[T1]
C. A. Naesseth, Nowcasting using Microblog Data. Bachelor's thesis, LiTH-ISY-EX-ET-12/0398, 2012.
Christian Andersson Naesseth

PhD Student in Automatic Control

(Swedish: Doktorand i reglerteknik)

Phone:
+46 13 281087
E-mail:
christian.a.naesseth_at_liu.se
Address:
Dept. of Electrical Engineering
Linköping University
SE-581 83 Linköping
Sweden
Visiting Address:
Campus Valla
Building B
Room 2A:522 (in the A corridor on the ground floor between entrance 25 and 27)


Page responsible: Christian Andersson Naesseth
Last updated: 2018-11-27