Department of Information Technology

Publications

Work in progress

[W1] Andreas Lindholm and Fredrik Lindsten. Learning dynamical systems with particle stochastic approximation EM. Submitted. Pre-print on arXiv.org.

[W2] Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten and Thomas B. Schön. Statistical Machine Learning - Supervised learning from data. Draft.

Journal papers

[J5] Andreas Lindholm, Dave Zachariah, Petre Stocia and Thomas B. Schön. Data Consistency Approach to Model Validation. IEEE Access 7(1) (2019), page 59788-59796. Open access@IEEE Xplore. Pre-print on arXiv.org, code on GitHub.

[J4] Dennis W. van der Meer, Mahmoud Shepero, Andreas Svensson, Joakim Widén, Joakim Munkhammar. Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes. ScienceDirect. Applied Energy 213 (2018), page 195-207.

[J3] Thomas B. Schön, Andreas Svensson, Lawrence Murray and Fredrik Lindsten, Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. Mechanical Systems and Signal Processing 104 (2018), page 866-883. ScienceDirect. Pre-print on arXiv.org. Code.

[J2] Andreas Svensson, Thomas B. Schön, Fredrik Lindsten, Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution. Mechanical Systems and Signal Processing 104 (2018), page 915-928. ScienceDirect. Pre-print on arXiv.org. Code.

[J1] Andreas Svensson, Thomas B. Schön, A flexible state space model for learning nonlinear dynamical systems. Automatica 80 (2017), page 189-199. ScienceDirect. arXiv.org (pre-print). Code is available at this page.

Conference papers (peer-reviewed)

[C9] Timothy J. Rogers, Thomas B. Schön, Andreas Lindholm, Keith Worden, Elizabeth J. Cross, Identification of a Duffing Oscillator Using Particle Gibbs with Ancestor Sampling. Presented at the XIIIth International Conference on Recent Advances in Structural Dynamics, Lyon, France, April, 2019. IOP Science.

[C8] Andreas Svensson, Dave Zachariah and Thomas B. Schön, How consistent is my model with the data? Information-Theoretic Model Check. Presented at the 18th IFAC symposium on system identification, Stockholm 2018. Pre-print on arXiv.org. Code on GitHub.

[C7] Andreas Svensson, Fredrik Lindsten and Thomas B. Schön, Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. Presented at the 18th IFAC symposium on system identification, Stockholm 2018. Pre-print on arXiv.org. Code.

[C6] Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön, Computationally Efficient Bayesian Learning of Gaussian Process State Space Models. In proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, May 2016, page 213-221. JMLR, arXiv.org. Material is available here.

[C5] Andreas Svensson, Thomas B. Schön, Arno Solin, Simo Särkkä, Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion. In proceedings of the 6th IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP), Cancun, Mexico, December 2015, page 481-484. Pre-print on arXiv.org. Matlab code, posters etc. IEEE Xplore

[C4] Andreas Svensson, Johan Dahlin, Thomas B. Schön. Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo. In proceedings of the 6th IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP), Cancun, Mexico, December 2015, page 477-480. Pre-print on arXiv.org. Matlab code and data. Poster. IEEE Xplore

[C3] Andreas Svensson, Thomas B. Schön, Manon Kok, Nonlinear state space smoothing using the conditional particle filter. In Proceedings of 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October, 2015, pre-print on arXiv.org. Matlab code. Presentation. ScienceDirect.

[C2] Thomas B. Schön, Fredrik Lindsten, Johan Dahlin, Johan Wågberg, Christian A. Naesseth, Andreas Svensson and Liang Dai, Sequential Monte Carlo Methods for System Identification. In Proceedings of 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October, 2015, pre-print on arXiv.org. ScienceDirect.

[C1] Andreas Svensson, Thomas B. Schön and Fredrik Lindsten, Identification of jump Markov linear models using particle filters. In Proceedings of 53rd IEEE Conference on Decision and Control (CDC), Los Angeles, CA, USA, December, 2014. IEEE Xplore, arXiv.org, PDF. Code, presentation, poster etc.

Technical reports

[TR3] Andreas Svensson, On the Role of Monte Carlo Methods in Swedish M. Sc. Engineering Education. Department of Information Technology, Uppsala University, Technical Report 2016-009.

[TR2] Andreas Svensson, Thomas B. Schön, Comparing Two Recent Particle Filter Implementations of Bayesian System Identification. Department of Information Technology, Uppsala University, Technical Report 2016-008. Presented at Reglermöte 2016. Code available.

[TR1] Andreas Svensson, Thomas B. Schön, Manon Kok, Some details on state space smoothing using the conditional particle filter. Department of Information Technology, Uppsala University, Technical Report 2015-019.

Theses

[PhD] Andreas Svensson, Machine learning with state-space models, Gaussian processes and Monte Carlo methods. PhD thesis. PDF.

[Lic] Andreas Svensson, Learning probabilistic models of dynamical
phenomena using particle filters. Licentiate thesis. PDF.


[MSc] Andreas Svensson, Model Predictive Control with Invariant Sets in Artificial Pancreas for Type 1 Diabetes Mellitus, Department of Electrical Engineering, Linköping University, 2013. LiTH-ISY-EX--13/4699--SE, available in DiVA.

[BSc] Andreas Svensson, Automatic Generation of Control Code for Flexible Automation, Department of Electrical Engineering, Linköping University, 2012. LiTH-ISY-EX-ET--12/0400--SE, available in DiVA.

Updated  2019-08-05 09:00:17 by Andreas Lindholm.