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 524
751 20 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
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CODE-II: a large-scale dataset for artificial intelligence in ECG analysis
Part of npj Digital Medicine, 2026
- DOI for CODE-II: a large-scale dataset for artificial intelligence in ECG analysis
- Download full text (pdf) of CODE-II: a large-scale dataset for artificial intelligence in ECG analysis
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Modeling of Biodiversity Dynamics After Forest Clear-cutting by Plot Aggregation
2026
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Safe Corridor Motion Planning for Dynamic Pick and Place Applications
Part of IEEE Control Systems Letters, p. 1189-1194, 2026
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Quantifying the impact of forestry practices on biodiversity using causal inference methods
2026
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A deep learning ECG model for identification and localization of occlusion myocardial infarction
Part of Nature Communications, 2026
- DOI for A deep learning ECG model for identification and localization of occlusion myocardial infarction
- Download full text (pdf) of A deep learning ECG model for identification and localization of occlusion myocardial infarction
All publications
Articles in journal
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CODE-II: a large-scale dataset for artificial intelligence in ECG analysis
Part of npj Digital Medicine, 2026
- DOI for CODE-II: a large-scale dataset for artificial intelligence in ECG analysis
- Download full text (pdf) of CODE-II: a large-scale dataset for artificial intelligence in ECG analysis
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Safe Corridor Motion Planning for Dynamic Pick and Place Applications
Part of IEEE Control Systems Letters, p. 1189-1194, 2026
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A deep learning ECG model for identification and localization of occlusion myocardial infarction
Part of Nature Communications, 2026
- DOI for A deep learning ECG model for identification and localization of occlusion myocardial infarction
- Download full text (pdf) of A deep learning ECG model for identification and localization of occlusion myocardial infarction
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Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations
Part of Transactions on Machine Learning Research, 2025
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Safe Bayesian Optimization Across Noise Models via Scenario Programming
Part of IEEE Control Systems Letters, p. 3029-3034, 2025
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Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization
Part of Advances Neural Information Processing Systems, 2025
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Automated segmentation of synchrotron-scanned fossils
Part of Fossil Record, p. 103-114, 2025
- DOI for Automated segmentation of synchrotron-scanned fossils
- Download full text (pdf) of Automated segmentation of synchrotron-scanned fossils
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Conditional sampling within generative diffusion models
Part of Philosophical Transactions. Series A, 2025
- DOI for Conditional sampling within generative diffusion models
- Download full text (pdf) of Conditional sampling within generative diffusion models
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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
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Part of Computers in cardiology, 2024
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Uncertainty Estimation with Recursive Feature Machines
Part of Proceedings of Machine Learning Research, p. 1408-1437, 2024
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Part of IFAC-PapersOnLine, p. 247-252, 2024
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Part of Neurocritical Care, p. 387-397, 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
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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
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On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches
Part of IEEE Control Systems Letters, p. 796-801, 2024
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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
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Safe Reinforcement Learning in Uncertain Contexts
Part of IEEE Transactions on robotics, p. 1828-1841, 2024
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Smoothed State Estimation via Efficient Solution of Linear Equations
Part of IEEE Transactions on Automatic Control, p. 5877-5889, 2023
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Late Breaking Abstract - automated cough analysis: The value of own data vs open sound databases
Part of European Respiratory Journal, 2023
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Variational Elliptical Processes
Part of Transactions on Machine Learning Research, 2023
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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
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Online Learning for Prediction via Covariance Fitting: Computation, Performance and Robustness
Part of Transactions on Machine Learning Research, 2023
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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
- Download full text (pdf) of On the trade-off between event-based and periodic state estimation under bandwidth constraints
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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
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On the regularization in DeePC
Part of IFAC-PapersOnLine, p. 625-631, 2023
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Neural motion planning in dynamic environments
Part of IFAC-PapersOnLine, p. 10126-10131, 2023
- DOI for Neural motion planning in dynamic environments
- Download full text (pdf) of Neural motion planning in dynamic environments
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Variational Elliptical Processes
Part of Transactions on Machine Learning Research, 2023
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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
- Download full text (pdf) of 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
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Invertible Kernel PCA With Random Fourier Features
Part of IEEE Signal Processing Letters, p. 563-567, 2023
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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
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Smoothed State Estimation via Efficient Solution of Linear Equations
Part of IEEE Transactions on Automatic Control, p. 5877-5889, 2023
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Overparameterized Linear Regression Under Adversarial Attacks
Part of IEEE Transactions on Signal Processing, p. 601-614, 2023
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Variational system identification for nonlinear state-space models
Part of Automatica, 2023
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Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
Part of IEEE Transactions on Signal Processing, p. 461-476, 2023
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Artificiell intelligens för kardiologer
Part of Svensk kardiologi, p. 20-25, 2022
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Incorporating Sum Constraints into Multitask Gaussian Processes
Part of Transactions on Machine Learning Research, p. 1-28, 2022
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Predicting Political Violence Using a State-Space Model
Part of International Interactions, p. 759-777, 2022
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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
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Part of IEEE Transactions on Signal Processing, p. 3676-3692, 2022
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Part of IEEE CONTROL SYSTEMS MAGAZINE, p. 75-102, 2022
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Data to Controller for Nonlinear Systems: An Approximate Solution
Part of IEEE Control Systems Letters, p. 1196-1201, 2022
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Part of IEEE Transactions on Visualization and Computer Graphics, p. 2602-2614, 2022
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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
- Download full text (pdf) of Memory efficient constrained optimization of scanning-beam lithography
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ResNet-based ECG Diagnosis of Myocardial Infarction in the Emergency Department
Part of Machine learning from ground truth: New medical imaging datasets for unsolved medical problems Workshop at NeurIPS, 2021
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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
- Download full text (pdf) of 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
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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
- Download full text (pdf) of 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
- Download full text (pdf) of 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
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Machine Learning in Risk Prediction
Part of Hypertension, p. 1165-1166, 2020
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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
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Learning Robust LQ-Controllers Using Application Oriented Exploration
Part of IEEE Control Systems Letters, p. 19-24, 2020
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Nonlinear Input Design as Optimal Control of a Hamiltonian System
Part of IEEE Control Systems Letters, p. 85-90, 2020
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The effect of interventions on COVID-19
Part of Nature, 2020
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On the smoothness of nonlinear system identification
Part of Automatica, 2020
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Smoothing With Couplings of Conditional Particle Filters
Part of Journal of the American Statistical Association, p. 721-729, 2020
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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
- Download full text (pdf) of 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
- Download full text (pdf) of 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
- Download full text (pdf) of 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
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A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors
Part of IEEE Signal Processing Letters, p. 1673-1677, 2019
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Elements of Sequential Monte Carlo
Part of FOUNDATIONS AND TRENDS IN MACHINE LEARNING, p. 187-306, 2019
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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
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High-Dimensional Filtering Using Nested Sequential Monte Carlo
Part of IEEE Transactions on Signal Processing, p. 4177-4188, 2019
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On model order priors for Bayesian identification of SISO linear systems
Part of International Journal of Control, p. 1645-1661, 2019
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Optimal controller/observer gains of discounted-cost LQG systems
Part of Automatica, p. 471-474, 2019
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Part of Mechanical systems and signal processing, p. 915-928, 2018
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Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
Part of Mechanical systems and signal processing, p. 866-883, 2018
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Probabilistic modelling and reconstruction of strain
Part of Nuclear Instruments and Methods in Physics Research Section B, p. 141-155, 2018
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Automated learning with a probabilistic programming language: Birch
Part of Annual Reviews in Control, p. 29-43, 2018
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Maximum likelihood identification of stable linear dynamical systems
Part of Automatica, p. 280-292, 2018
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Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes
Part of IEEE Transactions on robotics, p. 1112-1127, 2018
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Smoothed State Estimation via Efficient Solution of Linear Equations
Part of IFAC-PapersOnLine, p. 1613-1618, 2017
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System identification through online sparse Gaussian process regression with input noise
Part of IFAC Journal of Systems and Control, p. 1-11, 2017
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Divide-and-Conquer with sequential Monte Carlo
Part of Journal of Computational And Graphical Statistics, p. 445-458, 2017
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A flexible state–space model for learning nonlinear dynamical systems
Part of Automatica, p. 189-199, 2017
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On robust input design for nonlinear dynamical models
Part of Automatica, p. 268-278, 2017
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Rao–Blackwellized particle smoothers for conditionally linear Gaussian models
Part of IEEE Journal on Selected Topics in Signal Processing, p. 353-365, 2016
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Using convolution to estimate the score function for intractable state-transition models
Part of IEEE Signal Processing Letters, p. 498-501, 2016
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Mean and variance of the LQG cost function
Part of Automatica, p. 216-223, 2016
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Magnetometer calibration using inertial sensors
Part of IEEE Sensors Journal, p. 5679-5689, 2016
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A new structure exploiting derivation of recursive direct weight optimization
Part of IEEE Transactions on Automatic Control, p. 1683-1685, 2015
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Indoor positioning using ultrawideband and inertial measurements
Part of IEEE Transactions on Vehicular Technology, p. 1293-1303, 2015
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On the exponential convergence of the Kaczmarz algorithm
Part of IEEE Signal Processing Letters, p. 1571-1574, 2015
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Particle Metropolis–Hastings using gradient and Hessian information
Part of Statistics and computing, p. 81-92, 2015
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Particle Gibbs with ancestor sampling
Part of Journal of machine learning research, p. 2145-2184, 2014
Articles, review/survey
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Taming diffusion models for image restoration: a review
Part of Philosophical Transactions. Series A, 2025
- DOI for Taming diffusion models for image restoration: a review
- Download full text (pdf) of Taming diffusion models for image restoration: a review
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Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey
Part of Annual Reviews in Control, 2023
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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
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Machine learning: a first course for engineers and scientists
Cambridge University Press, 2022
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Using Inertial Sensors for Position and Orientation Estimation
Now Publishers Inc., 2017
Conference papers
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Quantifying the impact of forestry practices on biodiversity using causal inference methods
2026
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Is supervised learning really that different from un-supervised?
2026
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Learning dynamics from input-output data with Hamiltonian Gaussian processes.
2026
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Towards safe control parameter tuning in distributed multi-agent systems
Part of 2025 IEEE 64th Conference on Decision and Control (CDC), 2025
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Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
Part of Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), p. 1-15, 2025
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PACSBO: Probably Approximately Correct Safe Bayesian Optimization
Part of Systems Theory in Data and Optimization, p. 3-18, 2025
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Efficient Optimization Algorithms for Linear Adversarial Training
Part of International Conference on Artificial Intelligence and Statistics, p. 1207-1215, 2025
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Safe exploration in reproducing kernel Hilbert spaces
Part of International Conference on Artificial Intelligence and Statistics, 2025
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Conditioning diffusion models by explicit forward-backward bridging
Part of Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, p. 3709-3717, 2025
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Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
Part of 38th Conference on Neural Information Processing Systems, NeurIPS 2024, 2024
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Non-ergodicity in reinforcement learning: robustness via ergodicity transformations
2024
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Meta-state-space identification of stochastic hearts
Part of Book of Abstracts 43rd Benelux Meeting on Systems and Control, p. 230-230, 2024
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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
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Controlling Vision-Language Models for Multi-Task Image Restoration
Part of International Conference on Learning Representations 2024 (ICLR 2024), 2024
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No Double Descent in Principal Component Regression: A High-Dimensional Analysis
Part of International Conference on Machine Learning (ICML), p. 15271-15293, 2024
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Learning state observers for recurrent neural network models
Part of 2024 IEEE 63rd Conference on Decision and Control (CDC), p. 7871-7877, 2024
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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
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Entropy-regularized diffusion policy with Q-ensembles for offline reinforcement learning
Part of Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 2024
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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
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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
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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
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Uncertainty Estimation with Recursive Feature Machines
2024
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On feature learning of recursive feature machines and automatic relevance determination
2023
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Regularization properties of adversarially-trained linear regression
Part of Advances in Neural Information Processing Systems 36 (NeurIPS 2023), p. 23658-23670, 2023
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Image Restoration with Mean-Reverting Stochastic Differential Equations
Part of Proceedings of the 40th International Conference on Machine Learning, p. 23045-23066, 2023
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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report
Part of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
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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
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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
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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
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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
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Unsupervised dynamic modeling of medical image transformations
Part of 2022 25th International Conference on Information Fusion (FUSION 2022), p. 1-7, 2022
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Learning Proposals for Practical Energy-Based Regression
Part of International conference on artificial intelligence and statistics, vol 151, p. 4685-4704, 2022
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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
- Download full text (pdf) of Learning a Deformable Registration Pyramid
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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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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
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First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG
Part of 2021 Computing In Cardiology (CINC), 2021
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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
- Download full text (pdf) of Deep State Space Models for Nonlinear System Identification
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Willems' fundamental lemma based on second-order moments
Part of 2021 60th IEEE Conference On Decision And Control (CDC), p. 396-401, 2021
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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
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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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Part of IFAC PapersOnLine, p. 505-510, 2021
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Variational State and Parameter Estimation
Part of IFAC PapersOnLine, p. 732-737, 2021
- DOI for Variational State and Parameter Estimation
- Download full text (pdf) of Variational State and Parameter Estimation
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How to Train Your Energy-Based Model for Regression
2020
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Part of Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, p. 2370-2380, 2020
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Energy-Based Models for Deep Probabilistic Regression
Part of Computer Vision – ECCV 2020, p. 325-343, 2020
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Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble
Part of 2020 Computing in Cardiology, 2020
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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
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Optimistic robust linear quadratic dual control
Part of Proceedings of Machine Learning Research, VOL 120, p. 550-560, 2020
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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
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Part of The 35th Uncertainty in Artificial Intelligence Conference (UAI), p. 679-689, 2020
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Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood
Part of ICASSP 2020, p. 5860-5864, 2020
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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
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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
- Download full text (pdf) of 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
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Evaluating model calibration in classification
Part of 22nd International Conference on Artificial Intelligence and Statistics, p. 3459-3467, 2019
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Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Part of Proceedings of the 36th International Conference on Machine Learning, p. 4942-4950, 2019
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Deep convolutional networks in system identification
Part of Proc. 58th IEEE Conference on Decision and Control, p. 3670-3676, 2019
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Robust exploration in linear quadratic reinforcement learning
Part of Advances in Neural Information Processing Systems 32 (NIPS 2019), 2019
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Conditionally Independent Multiresolution Gaussian Processes
Part of 22nd International Conference On Artificial Intelligence And Statistics, Vol 89, 2019
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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
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Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations
p. 652-657, 2018
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Learning localized spatio-temporal models from streaming data
Part of Proceedings of the 35th International Conference on Machine Learning, p. 3927-3935, 2018
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Regularized parametric system identification: a decision-theoretic formulation
Part of 2018 Annual American Control Conference (ACC), p. 1895-1900, 2018
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Data-driven impulse response regularization via deep learning
p. 1-6, 2018
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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
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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
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Learning convex bounds for linear quadratic control policy synthesis
Part of Neural Information Processing Systems 2018, 2018
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How consistent is my model with the data?: Information-theoretic model check
p. 407-412, 2018
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Probabilistic programming allows for automated inference in factor graph models
2018
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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
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Linearly constrained Gaussian processes
Part of Proc. 31st Conference on Neural Information Processing Systems, p. 1215-1224, 2017
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On the construction of probabilistic Newton-type algorithms
Part of Proc. 56th Conference on Decision and Control, p. 6499-6504, 2017
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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
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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
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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
- Download full text (pdf) of 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
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A scalable and distributed solution to the inertial motion capture problem
Part of Proc. 19th International Conference on Information Fusion, p. 1348-1355, 2016
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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
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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
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Nonlinear state space smoothing using the conditional particle filter
Part of Proc. 17th IFAC Symposium on System Identification, p. 975-980, 2015
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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
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On Identification via EM with Latent Disturbances and Lagrangian Relaxation
p. 69-74, 2015
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Bayesian nonparametric identification of piecewise affine ARX systems
p. 709-714, 2015
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Identification of jump Markov linear models using particle filters
Part of Proc. 53rd Conference on Decision and Control, p. 6504-6509, 2014
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Backward sequential Monte Carlo for marginal smoothing
Part of Proc. 18th Workshop on Statistical Signal Processing, p. 368-371, 2014
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Robust auxiliary particle filters using multiple importance sampling
Part of Proc. 18th Workshop on Statistical Signal Processing, p. 268-271, 2014