Publications

Jarrad Courts, Adrian Wills, Thomas B. Schön and Brett Ninness. Variational system identification for nonlinear state-space models. Automatica, 2022. (Accepted for publication)

Andreas Lindholm, Johannes Hendriks, Adrian Wills and Thomas B. Schön. Predicting political violence using a state-space model. International Interactions, 2022. (Accepted for publication)

Carl Jidling, Adrian Wills, Andrew Flemming and Thomas B. Schön. Memory Efficient Constrained Optimization of Scanning-Beam Lithography. Optics Express, 30(12):20564–20579, 2022.

Joel Kronander, Daniel Jönsson, Jonas Unger, Thomas B. Schön and Magnus Wrenninge. Direct transmittance estimation in heterogeneous participating media using approximated Taylor expansions. IEEE Transactions on Visualization and Computer Graphics, 28(7):2602–2614, 2022.

Carl Jidling. Tailoring Gaussian processed and large-scale optimisation.
PhD thesis, Uppsala University, 2022. Diva

Carl Andersson. Deep probabilistic models for sequential and hierarchical data.
PhD thesis, Uppsala University, 2022. Diva

Daniel Lundén, Joey Öhman, Jan Kudlicka, Viktor Senderov, Fredrik Ronquist, David Broman. Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference. Proceedings of the 31st European Symposium on Programming (ESOP), Munich, April 2022. ESOP

Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten and Thomas B. Schön. Machine Learning – A first course for engineers and scientists. Cambridge University Press, 2022. PDF

Anna Wigren, Johan Wågberg, Fredrik Lindsten, Adrian Wills and Thomas B. Schön. Nonlinear system identification: Learning while respecting physical models using a Sequential Monte Carlo method. IEEE Control Systems Magazine (CSM), 42(1):75–102, 2022. IEEE

Fredrik K. Gustafsson, Martin Danelljan and Thomas B. Schön. Learning proposals for practical energy-based regression. In Proceedings of the 25nd International Conference on Artificial Intelligence and Statistics (AISTATS), Online, March, 2022. arXiv

Håkan Carlsson, Isaac Skog, Thomas B. Schön and Joakim Jaldén. Quantifying the uncertainty of the relative geometry in inertial sensors arrays. IEEE Sensors Journal, 21(17):19362– 19373, 2021.

Daniel Lundén, Johannes Borgström, David Broman. Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages. 30th European Symposium on Programming (ESOP), Online, March 2021. Springer

Jan Kudlicka. Probabilistic Programming for Birth-Death Models of Evolution. PhD thesis, Uppsala University, March, 2021. PDF

Adrian G.Wills and Thomas B.Schön. Stochastic quasi-Newton with line-search regularisation. Automatica, 127:109503, 2021. [PDF]

Antonio H. Ribeiro and Thomas B. Schön. How convolutional neural networks deal with aliasing. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Online, June, 2021. [IEEE]

Fredrik Ronquist, Jan Kudlicka, Viktor Senderov, Johannes Borgström, Nicolas Lartillot, Daniel Lundén, Lawrence Murray, Thomas B. Schön and David Broman. Probabilistic programming: a powerful new approach to statistical phylogenetics. Communications Biology, 4, 244, 2021.

Kristian Soltesz, Fredrik Gustafsson, Toomas Timpka, Joakim Jaldén, Carl Jidling, Albin Heimerson, Thomas B. Schön, Armin Spreco, Joakim Ekberg, Orjan Dahlström, Fredrik Bagge Carlson, Anna Jöud and Bo Bernhardsson. The effect of interventions on COVID-19. Nature, 588, E26–E28, 2020. Nature

Antonio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön and Luis A. Aguirre. On the smoothness of nonlinear system identification. Automatica, 121:109158, November 2020.

Pierre E. Jacob, Fredrik Lindsten and Thomas B. Schön. Smoothing with couplings of conditional particle filters. Journal of American Statistical Association (JASA), 115(530):721–729, 2020.

Jack Umenberger and Thomas B. Schön. Nonlinear input design as optimal control of a Hamiltonian system. IEEE Control Systems Letters, 4(1):85–90, 2020. Jointly published at the IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019.

Mina Ferizbegovic, Jack Umenberger, Håkan Hjalmarsson and Thomas B. Schön. Learning robust LQ-controllers using application oriented exploration. IEEE Control Systems Letters, 4(1):19–24, 2020. Jointly published at the IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019.

Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte and Thomas B. Schön. How to train your energy-based model for regression. In Proceedings of the 31st British Machine Vision Conference (BMVC), Online, September, 2020.

Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat and Thomas B. Schön. Energy-based models for deep probabilistic regression. In Proceedings of the European Conference on Computer Vision (ECCV), Online, August, 2020.

Fredrik K. Gustafsson, Martin Danelljan and Thomas B. Schön. Evaluating scalable Bayesian deep learning methods for robust computer vision. In 2nd Workshop on safe artificial intelligence for automated driving (SAIAD) at the conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, June, 2020.

Thomas B. Schön and Lennart Ljung. Deep learning in a system identification perspective. Encyclopedia of Systems and Control (Eds. T. Samad and J. Baillieul), Springer, 2020.

Thomas B. Schön. Nonlinear system identification using particle filters. Encyclopedia of Systems and Control (Eds. T. Samad and J. Baillieul), Springer, 2015, 2020. Springer

Jack Umenberger and Thomas B. Schön. Optimistic robust linear quadratic dual control. In Proceeding of Learning for dynamics and control (L4DC), Berkeley, CA, USA, June, 2020 (oral presentation). arXiv

Lennart Ljung, Carl Andersson, Koen Tiels, and Thomas B. Schön. Deep learning and system identification. In Proceedings of the IFAC World Congress, Berlin, Germany, July, 2020.

Adrian Wills, Thomas B. Schön and Carl Jidling. A fast quasi-Newton-type method for large-scale stochastic optimisation. In Proceedings of the IFAC World Congress, Berlin, Germany, July, 2020. arXiv

Antônio H. Ribeiro, Koen Tiels, Luis A. Aguirre and Thomas B. Schön. Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness. The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, June, 2020. arXiv

Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön and Fredrik Lindsten. Particle filter with rejection control and unbiased estimator of the marginal likelihood. In Proceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, May, 2020. arXiv

Manon Kok and Thomas B. Schön. A fast and robust algorithm for orientation estimation using inertial sensors. IEEE Signal Processing Letters, 26(11):1673-1677, 2019. arXiv IEEE code

Carl Jidling. Tailoring Gaussian processes for tomographic reconstruction. Licentiate thesis, Nr: 2019005, Uppsala University, Sweden, October, 2019. DiVA

Zenith Purisha, Carl Jidling, Niklas Wahlström, Simo Särkkä and Thomas B. Schön. Probabilistic approach to limited-data computed tomography reconstruction. Inverse Problems, 35(10): 105004, 2019. IOP

Johannes N. Hendriks, Carl Jidling, Thomas B. Schön, Adrian Wills, Christpher M. Wensrich and Erich H. Kisi. Neutron Transmission Strain Tomography for Non-Constant Strain-Free Lattice Spacing. Nuclear instruments and methods in physics research section B, 456:64-73, 2019. ScienceDirect

Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. High-dimensional filtering using nested sequential Monte Carlo. IEEE Transactions on Signal Processing, 67(16):4177-4188, 2019. IEEE

Andreas Lindholm, Dave Zachariah, Petre Stoica, and Thomas B. Schön. Data consistency approach to model validation. IEEE Access, 7:59788-59796, 2019. IEEE arXiv code

Patricio E. Valenzuela, Thomas B. Schön and Cristian R. Rojas. On model order priors for Bayesian identification of SISO linear systems. International Journal of Control, 92(7):1645-1661, 2019. IJC

Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson. Robust exploration in linear quadratic reinforcement learning. In Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019 (spotlight presentation). NeurIPS

Carl Andersson, Antonio H. Ribeiro, Koen Tiels, Niklas Wahlström and Thomas B. Schön. Deep convolutional networks are useful in system identification. In Proceedings of the IEEE 58th IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019. arXiv

Jack Umenberger, Thomas B. Schön and Fredrik Lindsten. Bayesian identification of state-space models via adaptive thermostats. In Proceedings of the IEEE 58th IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019. IEEE

Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist and Thomas B. Schön. Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Tel Aviv, Israel, July, 2019. arXiv Video

Muhammad Osama, Dave Zachariah and Thomas B. Schön. Inferring heterogeneous causal effects in presence of spatial confounding. In Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, June, 2019. ICML code

Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll and Thomas B. Schön. Evaluating model calibration in classification. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Japan, April, 2019. AISTATS

Timothy J. Rogers, Thomas B. Schön, Andreas Svensson, Keith Worden, and Elizabeth J. Cross. Identification of a Duffing oscillator using particle Gibbs with ancestor sampling. In International Conference on Recent Advances in Structural Dynamics (RASD), Lyon, France, April, 2019. IOP code

Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. Elements of Sequential Monte Carlo. Foundations and Trends in Machine Learning, 12(3):307-392, 2019. arXiv

Tarek Ahmed-Ali, Koen Tiels, Maarten Schoukens and Fouad Giri. Sampled-data adaptive observer for state-affine systems with uncertain output equation. Automatica, 103:96-105, 2019. ScienceDirect

Johan Dahlin and Thomas B. Schön. Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models. Journal of Statistical Software, 88(2):1–41, 2019. JSS

Hildo Bijl and Thomas B. Schön. Optimal controller/observer gains of discounted-cost LQG systems. Automatica, 101:471–474, 2019. arXiv ScienceDirect

Jack Umenberger and Thomas B. Schön. Learning convex bounds for linear quadratic control policy synthesis. In Neural Information Processing Systems (NIPS), Montréal, Canada, December 2018. NeurIPS

Lawrence Murray and Thomas B. Schön. Automated learning with a probabilistic programming language: Birch. Annual Reviews in Control, 46:29-43, 2018. arXiv ScienceDirect

Andreas Svensson. Machine learning with state-space models, Gaussian processes and Monte Carlo methods. PhD thesis, Uppsala University, October, 2018. PDF

Johan Wahlström, Isaac Skog and Peter Händel. Inertial Sensor Array Processing with Motion Models. 21st International Conference on Information Fusion (FUSION). IEEE, 2018.

Johan Wahlström, Joakim Jaldén, Isaac Skog and Peter Händel. Alternative EM Algorithms for Nonlinear State-Space Models. 21st International Conference on Information Fusion (FUSION), IEEE, 2018.

Jack Umenberger, Johan Wågberg, Ian R. Manchester and Thomas B. Schön. Maximum likelihood identification of stable linear dynamical systems. Automatica, 96:280-292, 2018. arXiv ScienceDirect

Muhammad Osama, Dave Zachariah and Thomas B. Schön. Learning localized spatio-temporal models from streaming data. In Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, July, 2018. ICML

Carl Jidling, Johannes Hendriks, Niklas Wahlström, Alexander Gregg, Thomas B. Schön, Chris Wensrich and Adrian Wills. Probabilistic modelling and reconstruction of strain. Nuclear instruments and methods in physics research section B, 436:141-155, 2018. arXiv ScienceDirect

Anna Wigren, Lawrence Murray and Fredrik Lindsten. Improving the particle filter for high-dimensional problems using artificial process noise. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018. arXiv

Andreas Svensson, Dave Zachariah and Thomas B. Schön. How consistent is my model with the data? Information-theoretic model check. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018. arXiv

Andreas Svensson, Fredrik Lindsten and Thomas B. Schön. Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018. arXiv

Marie Maros and Joakim Jaldén. ADMM for Distributed Dynamic Beamforming. IEEE Transactions on Signal and Information Processing over Networks, 4(2):220-235, 2018. IEEE

Johan Wågberg, Dave Zachariah and Thomas B. Schön. Regularized parametric system identification: a decision-theoretic formulation. In Proceedings of the American Control Conference (ACC), Milwaukee, WI, USA, June, 2018. arXiv

Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman and Thomas B. Schön. Delayed sampling and automatic Rao-Blackwellization of probabilistic programs. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Lanzarote, Spain, April, 2018. arXiv

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 (MSSP), 104:866-883, 2018. arXiv ScienceDirect

Andreas Svensson, Thomas B. Schön and Fredrik Lindsten. Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution. Mechanical Systems and Signal Processing (MSSP),
104:915-928, 2018. arXiv ScienceDirect

Carl Jidling, Niklas Wahlström, Adrian Wills and Thomas B. Schön. Linearly constrained Gaussian processes. Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA, December, 2017. arXiv NIPS Video

Adrian G. Wills and Thomas B. Schön. On the construction of probabilistic Newton-type algorithms. In Proceedings of the 56th IEEE Conference on Decision and Control (CDC), Melbourne, Australia, December, 2017. arXiv

Måns Magnusson, Leif Jonsson, Mattias Villani and David Broman. Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models, Journal of Computational and Graphical Statistics, 2017. JCGS

Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden and Michel Verhaegen. System identification through online sparse Gaussian process regression with input noise. IFAC Journal of Systems and Control, 2:1-11, December, 2017. ScienceDirect

Manon Kok, Jeroen Hol and Thomas B. Schön. Using inertial sensors for position and orientation estimation. Foundations and Trends in Signal Processing, 11(1-2):1-153, 2017. arXiv Now

Håkan Carlsson, Isaac Skog and Joakim Jaldén, On-the-fly geometric calibration of inertial sensor arrays, In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), September, Sapporo, Japan, 2017.

Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth, Bonnie Kirkpatrick, Thomas B. Schön, John Aston and Alexandre Bouchard-Côté. Divide-and-Conquer with Sequential Monte Carlo. Journal of Computational and Graphical Statistics (JCGS), 26(2):445-458, 2017. JCGS

Li-Hui Geng, Brett Ninness, Adrian G. Wills, Thomas B. Schön. Smoothed state estimation via efficient solution of linear equations. In Proceedings of the 20th World Congress of the International Federation of Automatic Control (IFAC), Toulouse, France, July, 2017. ScienceDirect

Andreas Svensson and Thomas B. Schön. A flexible state space model for learning nonlinear dynamical systems. Automatica, 80:189-199, June, 2017. Automatica

Johan Wågberg, Dave Zachariah, Thomas B. Schön and Petre Stoica. Prediction performance after learning in Gaussian process regression. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA, April, 2017. PMLR

Andreas Svensson. Learning probabilistic models of dynamical phenomena using particle filters. Licentiate thesis, Nr: 2016011, Uppsala University, Sweden, December, 2016. DiVA