Thomas Schön
Professor at Department of Information Technology; Division of Systems and Control
- Telephone:
- +46 18 471 25 94
- Mobile phone:
- +46 73 593 38 87
- E-mail:
- thomas.schon@it.uu.se
- Visiting address:
- Hus 10, Regementsvägen 10
- Postal address:
- Box 337
751 05 UPPSALA
- Academic merits:
- Docent
Short presentation
Thomas B. Schön is the Beijer Professor of Artificial Intelligence at the Department of Information Technology at Uppsala University and currently heads the five-year project AI4Research. He received his PhD degree in Automatic Control in Feb. 2006 from Linköping University, and has held visiting positions with the University of Cambridge (UK), the University of Newcastle (Australia) and Universidad Técnica Federico Santa María (Valparaíso, Chile)
Keywords
- artificial intelligence
- machine learning
- automatic control
- deep learning
- signal processing
- computer vision
- data analytics
- decision-making with algorithms
- artificiell intelligens
- maskininlärning
- ai4research
- reglerteknik
- signalbehandling
- datorseende
Biography
Thomas B. Schön is the Beijer Professor of Artificial Intelligence at the Department of Information Technology at Uppsala University and currently heads the five-year project AI4Research. He received his PhD degree in Automatic Control in Feb. 2006, his MSc degree in Applied Physics and Electrical Engineering in Sep. 2001, his BSc degree in Business Administration and Economics in Jan. 2001, all from Linköping University. He has held visiting positions with the University of Cambridge (UK), the University of Newcastle (Australia) and Universidad Técnica Federico Santa María (Valparaíso, Chile). In 2018, he was elected to The Royal Swedish Academy of Engineering Sciences (IVA) and The Royal Society of Sciences in Uppsala. He received the Tage Erlander prize for natural sciences and technology in 2017 and the Arnberg prize in 2016, both awarded by the Royal Swedish Academy of Sciences (KVA). He was awarded the Automatica Best Paper Prize in 2014, and in 2013 he received the best PhD thesis award by The European Association for Signal Processing. He received the best teacher award at the Institute of Technology, Linköping University in 2009. He is a Senior member of the IEEE and a fellow of the ELLIS society.
Schön has a broad interest in developing new algorithms and mathematical models capable of learning and acting based on data. His main scientific field is Machine Learning, but he also regularly publishes in other fields such as statistics, automatic control, signal processing and computer vision. He pursues both basic research and applied research, where the latter is typically carried out in collaboration with industry or applied research groups.

Publications
Recent publications
Underlätta samarbeten mellan företag och universitet
2025
Automated segmentation of synchrotron-scanned fossils
Part of Fossil Record, p. 103-114, 2025
- DOI for Automated segmentation of synchrotron-scanned fossils
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Rättsliga hinder mot samverkan?: Juridik och ledningsfrågor vid doktorandsamverkan
2025
Deep networks for system identification: A survey
Part of Automatica, 2025
- DOI for Deep networks for system identification: A survey
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A Model Predictive Control Approach to Motion Planning in Dynamic Environments
Part of 2024 European Control Conference (ECC), p. 3247-3254, 2024
All publications
Articles in journal
Automated segmentation of synchrotron-scanned fossils
Part of Fossil Record, p. 103-114, 2025
- DOI for Automated segmentation of synchrotron-scanned fossils
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Deep networks for system identification: A survey
Part of Automatica, 2025
- DOI for Deep networks for system identification: A survey
- Download full text (pdf) of Deep networks for system identification: A survey
Uncertainty Estimation with Recursive Feature Machines
Part of Proceedings of Machine Learning Research, p. 1408-1437, 2024
Rao-Blackwellized particle smoothing for simultaneous localization and mapping
Part of DATA-CENTRIC ENGINEERING, 2024
- DOI for Rao-Blackwellized particle smoothing for simultaneous localization and mapping
- Download full text (pdf) of Rao-Blackwellized particle smoothing for simultaneous localization and mapping
On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches
Part of IEEE Control Systems Letters, p. 796-801, 2024
Part of IFAC-PapersOnLine, p. 247-252, 2024
Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
Part of Scientific Reports, 2024
- DOI for Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
- Download full text (pdf) of Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
Part of Neurocritical Care, 2024
- DOI for Machine Learning Based Prediction of Imminent ICP Insults During Neurocritical Care of Traumatic Brain Injury
- Download full text (pdf) of Machine Learning Based Prediction of Imminent ICP Insults During Neurocritical Care of Traumatic Brain Injury
Safe Reinforcement Learning in Uncertain Contexts
Part of IEEE Transactions on robotics, p. 1828-1841, 2024
Screening for Chagas disease from the electrocardiogram using a deep neural network
Part of PLoS Neglected Tropical Diseases, 2023
- DOI for Screening for Chagas disease from the electrocardiogram using a deep neural network
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How Reliable is Your Regression Model’s Uncertainty Under Real-World Distribution Shifts?
Part of Transactions on Machine Learning Research, 2023
Variational Elliptical Processes
Part of Transactions on Machine Learning Research, 2023
Gaussian inference for data-driven state-feedback design of nonlinear systems
Part of IFAC-PapersOnLine, p. 4796-4803, 2023
- DOI for Gaussian inference for data-driven state-feedback design of nonlinear systems
- Download full text (pdf) of Gaussian inference for data-driven state-feedback design of nonlinear systems
Neural motion planning in dynamic environments
Part of IFAC-PapersOnLine, p. 10126-10131, 2023
- DOI for Neural motion planning in dynamic environments
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On the regularization in DeePC
Part of IFAC-PapersOnLine, p. 625-631, 2023
On the trade-off between event-based and periodic state estimation under bandwidth constraints
Part of IFAC-PapersOnLine, p. 5275-5280, 2023
- DOI for On the trade-off between event-based and periodic state estimation under bandwidth constraints
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Invertible Kernel PCA With Random Fourier Features
Part of IEEE Signal Processing Letters, p. 563-567, 2023
Online Learning for Prediction via Covariance Fitting: Computation, Performance and Robustness
Part of Transactions on Machine Learning Research, 2023
Part of Computer Methods in Applied Mechanics and Engineering, 2023
- DOI for Inferring the probability distribution over strain tensors in polycrystals from diffraction based measurements
- Download full text (pdf) of Inferring the probability distribution over strain tensors in polycrystals from diffraction based measurements
Smoothed State Estimation via Efficient Solution of Linear Equations
Part of IEEE Transactions on Automatic Control, p. 5877-5889, 2023
Overparameterized Linear Regression Under Adversarial Attacks
Part of IEEE Transactions on Signal Processing, p. 601-614, 2023
Variational system identification for nonlinear state-space models
Part of Automatica, 2023
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
Part of IEEE Transactions on Signal Processing, p. 461-476, 2023
Predicting Political Violence Using a State-Space Model
Part of International Interactions, p. 759-777, 2022
Part of Scientific Reports, 2022
- DOI for Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
- Download full text (pdf) of Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
Part of IEEE Transactions on Visualization and Computer Graphics, p. 2602-2614, 2022
Data to Controller for Nonlinear Systems: An Approximate Solution
Part of IEEE Control Systems Letters, p. 1196-1201, 2022
Part of IEEE CONTROL SYSTEMS MAGAZINE, p. 75-102, 2022
Part of IEEE Transactions on Signal Processing, p. 3676-3692, 2022
Memory efficient constrained optimization of scanning-beam lithography
Part of Optics Express, p. 20564-20579, 2022
- DOI for Memory efficient constrained optimization of scanning-beam lithography
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Deep neural network-estimated electrocardiographic age as a mortality predictor
Part of Nature Communications, 2021
- DOI for Deep neural network-estimated electrocardiographic age as a mortality predictor
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Stochastic quasi-Newton with line-search regularisation
Part of Automatica, 2021
Universal probabilistic programming offers a powerful approach to statistical phylogenetics
Part of Communications Biology, 2021
- DOI for Universal probabilistic programming offers a powerful approach to statistical phylogenetics
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Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays
Part of IEEE Sensors Journal, p. 19362-19373, 2021
- DOI for Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays
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Gaussian Variational State Estimation for Nonlinear State-Space Models
Part of IEEE Transactions on Signal Processing, p. 5979-5993, 2021
Automatic diagnosis of the 12-lead ECG using a deep neural network
Part of Nature Communications, 2020
- DOI for Automatic diagnosis of the 12-lead ECG using a deep neural network
- Download full text (pdf) of Automatic diagnosis of the 12-lead ECG using a deep neural network
Machine Learning in Risk Prediction
Part of Hypertension, p. 1165-1166, 2020
Nonlinear Input Design as Optimal Control of a Hamiltonian System
Part of IEEE Control Systems Letters, p. 85-90, 2020
Learning Robust LQ-Controllers Using Application Oriented Exploration
Part of IEEE Control Systems Letters, p. 19-24, 2020
The effect of interventions on COVID-19
Part of Nature, 2020
On the smoothness of nonlinear system identification
Part of Automatica, 2020
Smoothing With Couplings of Conditional Particle Filters
Part of Journal of the American Statistical Association, p. 721-729, 2020
Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models
Part of Journal of Statistical Software, p. 1-41, 2019
- DOI for Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models
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Data Consistency Approach to Model Validation
Part of IEEE Access, p. 59788-59796, 2019
- DOI for Data Consistency Approach to Model Validation
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Identification of a Duffing oscillator using particle Gibbs with ancestor sampling
Part of Journal of Physics, Conference Series, 2019
- DOI for Identification of a Duffing oscillator using particle Gibbs with ancestor sampling
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Probabilistic approach to limited-data computed tomography reconstruction
Part of Inverse Problems, 2019
A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors
Part of IEEE Signal Processing Letters, p. 1673-1677, 2019
Elements of Sequential Monte Carlo
Part of FOUNDATIONS AND TRENDS IN MACHINE LEARNING, p. 187-306, 2019
Neutron transmission strain tomography for non-constant stress-free lattice spacing
Part of Nuclear Instruments and Methods in Physics Research Section B, p. 64-73, 2019
High-Dimensional Filtering Using Nested Sequential Monte Carlo
Part of IEEE Transactions on Signal Processing, p. 4177-4188, 2019
On model order priors for Bayesian identification of SISO linear systems
Part of International Journal of Control, p. 1645-1661, 2019
Optimal controller/observer gains of discounted-cost LQG systems
Part of Automatica, p. 471-474, 2019
Part of Mechanical systems and signal processing, p. 915-928, 2018
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
Part of Mechanical systems and signal processing, p. 866-883, 2018
Probabilistic modelling and reconstruction of strain
Part of Nuclear Instruments and Methods in Physics Research Section B, p. 141-155, 2018
Automated learning with a probabilistic programming language: Birch
Part of Annual Reviews in Control, p. 29-43, 2018
Maximum likelihood identification of stable linear dynamical systems
Part of Automatica, p. 280-292, 2018
Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes
Part of IEEE Transactions on robotics, p. 1112-1127, 2018
System identification through online sparse Gaussian process regression with input noise
Part of IFAC Journal of Systems and Control, p. 1-11, 2017
Divide-and-Conquer with sequential Monte Carlo
Part of Journal of Computational And Graphical Statistics, p. 445-458, 2017
Smoothed State Estimation via Efficient Solution of Linear Equations
Part of IFAC-PapersOnLine, p. 1613-1618, 2017
A flexible state–space model for learning nonlinear dynamical systems
Part of Automatica, p. 189-199, 2017
On robust input design for nonlinear dynamical models
Part of Automatica, p. 268-278, 2017
Rao–Blackwellized particle smoothers for conditionally linear Gaussian models
Part of IEEE Journal on Selected Topics in Signal Processing, p. 353-365, 2016
Using convolution to estimate the score function for intractable state-transition models
Part of IEEE Signal Processing Letters, p. 498-501, 2016
Mean and variance of the LQG cost function
Part of Automatica, p. 216-223, 2016
Magnetometer calibration using inertial sensors
Part of IEEE Sensors Journal, p. 5679-5689, 2016
A new structure exploiting derivation of recursive direct weight optimization
Part of IEEE Transactions on Automatic Control, p. 1683-1685, 2015
Indoor positioning using ultrawideband and inertial measurements
Part of IEEE Transactions on Vehicular Technology, p. 1293-1303, 2015
On the exponential convergence of the Kaczmarz algorithm
Part of IEEE Signal Processing Letters, p. 1571-1574, 2015
Particle Metropolis–Hastings using gradient and Hessian information
Part of Statistics and computing, p. 81-92, 2015
Particle Gibbs with ancestor sampling
Part of Journal of machine learning research, p. 2145-2184, 2014
Articles, review/survey
Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey
Part of Annual Reviews in Control, 2023
Sequential Monte Carlo: A Unified Review
Part of ANNUAL REVIEW OF CONTROL ROBOTICS AND AUTONOMOUS SYSTEMS, p. 159-182, 2023
- DOI for Sequential Monte Carlo: A Unified Review
- Download full text (pdf) of Sequential Monte Carlo: A Unified Review
Books
Machine learning: a first course for engineers and scientists
Cambridge University Press, 2022
Using Inertial Sensors for Position and Orientation Estimation
Now Publishers Inc., 2017
Conference papers
A Model Predictive Control Approach to Motion Planning in Dynamic Environments
Part of 2024 European Control Conference (ECC), p. 3247-3254, 2024
Online Learning in Motion Modeling for Intra-interventional Image Sequences
Part of Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, p. 706-716, 2024
- DOI for Online Learning in Motion Modeling for Intra-interventional Image Sequences
- Download full text (pdf) of Online Learning in Motion Modeling for Intra-interventional Image Sequences
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models
Part of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2024, p. 6641-6651, 2024
Controlling Vision-Language Models for Multi-Task Image Restoration
2024
NTIRE 2024 restore any image model (RAIM) in the wild challenge
Part of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2024, p. 6632-6640, 2024
No Double Descent in Principal Component Regression: A High-Dimensional Analysis
Part of International Conference on Machine Learning (ICML), 2024
Entropy-regularized diffusion policy with Q-ensembles for offline reinforcement learning
Part of Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 2024
On Feynman–Kac training of partial Bayesian neural networks
Part of Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), p. 3223-3231, 2024
Uncertainty Estimation with Recursive Feature Machines
2024
Regularization properties of adversarially-trained linear regression
Part of Advances in Neural Information Processing Systems 36 (NeurIPS 2023), p. 23658-23670, 2023
NTIRE 2023 Image Shadow Removal Challenge Report
Part of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), p. 1788-1807, 2023
Lens-to-Lens Bokeh Effect Transformation: NTIRE 2023 Challenge Report
Part of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, p. 1643-1659, 2023
NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report
Part of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
Refusion: Enabling Large-Size Realistic Image Restoration With Latent-Space Diffusion Models
Part of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), p. 1680-1691, 2023
NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results
Part of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), p. 1346-1372, 2023
Unsupervised dynamic modeling of medical image transformations
Part of 2022 25th International Conference on Information Fusion (FUSION 2022), p. 1-7, 2022
Learning Proposals for Practical Energy-Based Regression
Part of International conference on artificial intelligence and statistics, vol 151, p. 4685-4704, 2022
Learning a Deformable Registration Pyramid
Part of Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, p. 80-86, 2021
- DOI for Learning a Deformable Registration Pyramid
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Accurate 3D Object Detection using Energy-Based Models
Part of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recogition Workshops (CVPRW 2021), p. 2849-2858, 2021
Learning deep autoregressive models for hierarchical data
Part of IFAC PapersOnLine, p. 529-534, 2021
- DOI for Learning deep autoregressive models for hierarchical data
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Deep State Space Models for Nonlinear System Identification
Part of IFAC PapersOnLine, p. 481-486, 2021
- DOI for Deep State Space Models for Nonlinear System Identification
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First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG
Part of 2021 Computing In Cardiology (CINC), 2021
Part of IFAC PapersOnLine, p. 505-510, 2021
How convolutional neural networks deal with aliasing
Part of 2021 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP 2021), p. 2755-2759, 2021
Willems' fundamental lemma based on second-order moments
Part of 2021 60th IEEE Conference On Decision And Control (CDC), p. 396-401, 2021
Variational State and Parameter Estimation
Part of IFAC PapersOnLine, p. 732-737, 2021
- DOI for Variational State and Parameter Estimation
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Bayes Control of Hammerstein Systems
Part of IFAC PapersOnLine, p. 755-760, 2021
- DOI for Bayes Control of Hammerstein Systems
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Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
Part of IFAC PapersOnLine, p. 97-102, 2021
- DOI for Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
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Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble
Part of 2020 Computing in Cardiology, 2020
Energy-Based Models for Deep Probabilistic Regression
2020
How to Train Your Energy-Based Model for Regression
2020
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Part of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2020), p. 1289-1298, 2020
Optimistic robust linear quadratic dual control
Part of Proceedings of Machine Learning Research, VOL 120, p. 550-560, 2020
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness
Part of Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), p. 2370-2380, 2020
Part of The 35th Uncertainty in Artificial Intelligence Conference (UAI), p. 679-689, 2020
Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood
Part of ICASSP 2020, p. 5860-5864, 2020
Deep Learning and System Identification
Part of IFAC Papersonline, p. 1175-1181, 2020
- DOI for Deep Learning and System Identification
- Download full text (pdf) of Deep Learning and System Identification
A fast quasi-Newton-type method for large-scale stochastic optimisation
Part of IFAC PapersOnline, p. 1249-1254, 2020
- DOI for A fast quasi-Newton-type method for large-scale stochastic optimisation
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Bayesian identification of state-space models via adaptive thermostats
Part of 2019 IEEE 58th conference on decision and control (CDC), p. 7382-7388, 2019
Evaluating model calibration in classification
Part of 22nd International Conference on Artificial Intelligence and Statistics, p. 3459-3467, 2019
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Part of Proceedings of the 36th International Conference on Machine Learning, p. 4942-4950, 2019
Deep convolutional networks in system identification
Part of Proc. 58th IEEE Conference on Decision and Control, p. 3670-3676, 2019
Robust exploration in linear quadratic reinforcement learning
Part of Advances in Neural Information Processing Systems 32 (NIPS 2019), 2019
Conditionally Independent Multiresolution Gaussian Processes
Part of 22nd International Conference On Artificial Intelligence And Statistics, Vol 89, 2019
Automatic diagnosis of short-duration 12-lead ECG using a deep convolutional network
Part of ML4H: Machine Learning for Health Workshop at NeurIPS, Montréal, Canada, December 2018., 2018
Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations
p. 652-657, 2018
Learning localized spatio-temporal models from streaming data
Part of Proceedings of the 35th International Conference on Machine Learning, p. 3927-3935, 2018
Regularized parametric system identification: a decision-theoretic formulation
Part of 2018 Annual American Control Conference (ACC), p. 1895-1900, 2018
Data-driven impulse response regularization via deep learning
p. 1-6, 2018
Delayed sampling and automatic Rao-Blackwellization of probabilistic programs
Part of Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Lanzarote, Spain, April, 2018, 2018
Auxiliary-Particle-Filter-based Two-Filter Smoothing for Wiener State-Space Models
Part of Proceedings of the 21st International Conference on Information Fusion, Cambridge, UK, July, 2018., p. 1904-1911, 2018
Learning convex bounds for linear quadratic control policy synthesis
Part of Neural Information Processing Systems 2018, 2018
How consistent is my model with the data?: Information-theoretic model check
p. 407-412, 2018
Probabilistic programming allows for automated inference in factor graph models
2018
Linearly constrained Gaussian processes
Part of Proc. 31st Conference on Neural Information Processing Systems, p. 1215-1224, 2017
Prediction Performance After Learning in Gaussian Process Regression
Part of Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, p. 1264-1272, 2017
On the construction of probabilistic Newton-type algorithms
Part of Proc. 56th Conference on Decision and Control, p. 6499-6504, 2017
Particle-based Gaussian process optimization for input design in nonlinear dynamical models
Part of 2016 IEEE 55th Conference On Decision And Control (CDC), p. 2085-2090, 2016
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Part of Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, p. 213-221, 2016
Accelerometer calibration using sensor fusion with a gyroscope
Part of Proc. 19th Statistical Signal Processing Workshop, p. 660-664, 2016
- DOI for Accelerometer calibration using sensor fusion with a gyroscope
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Prediction performance after learning in Gaussian process regression
Part of 25th European Research Network System Identification Workshop, 2016
A scalable and distributed solution to the inertial motion capture problem
Part of Proc. 19th International Conference on Information Fusion, p. 1348-1355, 2016
Marginalizing Gaussian process hyperparameters using sequential Monte Carlo
Part of Proc. 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, p. 477-480, 2015
- DOI for Marginalizing Gaussian process hyperparameters using sequential Monte Carlo
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Particle filtering based identification for autonomous nonlinear ODE models
Part of Proc. 17th IFAC Symposium on System Identification, p. 415-420, 2015
Nonlinear state space model identification using a regularized basis function expansion
Part of Proc. 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, p. 481-484, 2015
Nonlinear state space smoothing using the conditional particle filter
Part of Proc. 17th IFAC Symposium on System Identification, p. 975-980, 2015
Bayesian nonparametric identification of piecewise affine ARX systems
p. 709-714, 2015
On Identification via EM with Latent Disturbances and Lagrangian Relaxation
p. 69-74, 2015
Identification of jump Markov linear models using particle filters
Part of Proc. 53rd Conference on Decision and Control, p. 6504-6509, 2014
Backward sequential Monte Carlo for marginal smoothing
Part of Proc. 18th Workshop on Statistical Signal Processing, p. 368-371, 2014
Robust auxiliary particle filters using multiple importance sampling
Part of Proc. 18th Workshop on Statistical Signal Processing, p. 268-271, 2014