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ORCIDThomas B. Schön
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- affiliation: Uppsala University, Sweden
- affiliation (former): Linköping University, Department of Electrical Engineering
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2020 – today
- 2023
[j48]Jarrad Courts, Adrian G. Wills, Thomas B. Schön, Brett Ninness:
Variational system identification for nonlinear state-space models. Autom. 147: 110687 (2023)- [j47]Zheng Zhao
, Simo Särkkä, Jens Sjölund, Thomas B. Schön:
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals. IEEE Trans. Signal Process. 71: 461-476 (2023) - [j46]Antônio H. Ribeiro, Thomas B. Schön:
Overparameterized Linear Regression Under Adversarial Attacks. IEEE Trans. Signal Process. 71: 601-614 (2023) - [i77]Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön:
Image Restoration with Mean-Reverting Stochastic Differential Equations. CoRR abs/2301.11699 (2023) - [i76]Gianluigi Pillonetto, Aleksandr Y. Aravkin, Daniel Gedon, Lennart Ljung, Antônio H. Ribeiro, Thomas B. Schön:
Deep networks for system identification: a Survey. CoRR abs/2301.12832 (2023) - [i75]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? CoRR abs/2302.03679 (2023) - [i74]Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström, Thomas B. Schön:
Invertible Kernel PCA with Random Fourier Features. CoRR abs/2303.05043 (2023) - 2022
- [j45]Johannes N. Hendriks, James R. Z. Holdsworth, Adrian G. Wills, Thomas B. Schön, Brett Ninness:
Data to Controller for Nonlinear Systems: An Approximate Solution. IEEE Control. Syst. Lett. 6: 1196-1201 (2022) - [j44]Conor Rosato, Lee Devlin, Vincent Béraud, Paul R. Horridge, Thomas B. Schön, Simon Maskell:
Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters. IEEE Trans. Signal Process. 70: 3676-3692 (2022) - [j43]Daniel Jönsson, Joel Kronander, Jonas Unger, Thomas B. Schön, Magnus Wrenninge:
Direct Transmittance Estimation in Heterogeneous Participating Media Using Approximated Taylor Expansions. IEEE Trans. Vis. Comput. Graph. 28(7): 2602-2614 (2022) - [c72]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Learning Proposals for Practical Energy-Based Regression. AISTATS 2022: 4685-4704 - [c71]Niklas Gunnarsson, Jens Sjölund, Peter Kimstrand, Thomas B. Schön:
Unsupervised dynamic modeling of medical image transformations. FUSION 2022: 1-7 - [i73]Philipp Pilar, Carl Jidling, Thomas B. Schön, Niklas Wahlström:
Incorporating Sum Constraints into Multitask Gaussian Processes. CoRR abs/2202.01793 (2022) - [i72]Antônio H. Ribeiro, Thomas B. Schön:
Overparameterized Linear Regression under Adversarial Attacks. CoRR abs/2204.06274 (2022) - [i71]Antônio H. Ribeiro, Dave Zachariah, Thomas B. Schön:
Surprises in adversarially-trained linear regression. CoRR abs/2205.12695 (2022) - [i70]Tim Martin, Thomas B. Schön, Frank Allgöwer:
Gaussian inference for data-driven state-feedback design of nonlinear systems. CoRR abs/2211.05639 (2022) - [i69]Philipp Von Bachmann, Daniel Gedon, Fredrik K. Gustafsson, Antônio H. Ribeiro, Erik Lampa, Stefan Gustafsson, Johan Sundström, Thomas B. Schön:
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods. CoRR abs/2212.13890 (2022) - 2021
- [j42]Adrian G. Wills, Thomas B. Schön:
Stochastic quasi-Newton with line-search regularisation. Autom. 127: 109503 (2021) - [j41]Jarrad Courts, Adrian G. Wills, Thomas B. Schön:
Gaussian Variational State Estimation for Nonlinear State-Space Models. IEEE Trans. Signal Process. 69: 5979-5993 (2021) - [c70]Mina Ferizbegovic, Håkan Hjalmarsson, Per Mattsson, Thomas B. Schön:
Willems' fundamental lemma based on second-order moments. CDC 2021: 396-401 - [c69]Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström, Thomas B. Schön:
First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG. CinC 2021: 1-4 - [c68]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Accurate 3D Object Detection Using Energy-Based Models. CVPR Workshops 2021: 2855-2864 - [c67]Antônio H. Ribeiro, Thomas B. Schön:
How Convolutional Neural Networks Deal with Aliasing. ICASSP 2021: 2755-2759 - [i68]Antônio H. Ribeiro, Thomas B. Schön:
How Convolutional Neural Networks Deal with Aliasing. CoRR abs/2102.07757 (2021) - [i67]Filip de Roos, Carl Jidling, Adrian Wills, Thomas B. Schön, Philipp Hennig:
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization. CoRR abs/2102.10880 (2021) - [i66]Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön:
Latent linear dynamics in spatiotemporal medical data. CoRR abs/2103.00930 (2021) - [i65]Johannes N. Hendriks, James R. Z. Holdsworth, Adrian G. Wills, Thomas B. Schön, Brett Ninness:
Data to Controller for Nonlinear Systems: An Approximate Solution. CoRR abs/2103.08782 (2021) - [i64]Carl R. Andersson, Niklas Wahlström, Thomas B. Schön:
Learning deep autoregressive models for hierarchical data. CoRR abs/2104.13853 (2021) - [i63]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Learning Proposals for Practical Energy-Based Regression. CoRR abs/2110.11948 (2021) - [i62]Conor Rosato, Paul R. Horridge, Thomas B. Schön, Simon Maskell:
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters. CoRR abs/2111.01409 (2021) - 2020
- [j40]Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis Antonio Aguirre:
On the smoothness of nonlinear system identification. Autom. 121: 109158 (2020) - [j39]Mina Ferizbegovic, Jack Umenberger, Håkan Hjalmarsson, Thomas B. Schön:
Learning Robust LQ-Controllers Using Application Oriented Exploration. IEEE Control. Syst. Lett. 4(1): 19-24 (2020) - [j38]Jack Umenberger, Thomas B. Schön:
Nonlinear Input Design as Optimal Control of a Hamiltonian System. IEEE Control. Syst. Lett. 4(1): 85-90 (2020) - [j37]Kristian Soltesz, Fredrik Gustafsson, Toomas Timpka, Joakim Jaldén, Carl Jidling, Albin Heimerson, Thomas B. Schön, Armin Spreco, Joakim Ekberg, Örjan Dahlström, Fredrik Bagge Carlson, Anna Jöud, Bo Bernhardsson:
The effect of interventions on COVID-19. Nat. 588(7839): E26-E28 (2020) - [c66]Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön:
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness. AISTATS 2020: 2370-2380 - [c65]Fredrik Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön:
How to Train Your Energy-Based Model for Regression. BMVC 2020 - [c64]Antônio H. Ribeiro, Daniel Gedon, Daniel Martins Teixeira, Manoel Horta Ribeiro, Antônio Luiz Pinho Ribeiro, Thomas B. Schön, Wagner Meira Jr.:
Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble. CinC 2020: 1-4 - [c63]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision. CVPR Workshops 2020: 1289-1298 - [c62]Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, Thomas B. Schön:
Energy-Based Models for Deep Probabilistic Regression. ECCV (20) 2020: 325-343 - [c61]Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman P. Pflugfelder, Joni-Kristian Kämäräinen, Martin Danelljan, Luka Cehovin Zajc, Alan Lukezic, Ondrej Drbohlav, Linbo He, Yushan Zhang, Song Yan, Jinyu Yang, Gustavo Fernández, Alexander G. Hauptmann, Alireza Memarmoghadam, Álvaro García-Martín, Andreas Robinson, Anton Varfolomieiev, Awet Haileslassie Gebrehiwot, Bedirhan Uzun, Bin Yan, Bing Li, Chen Qian, Chi-Yi Tsai, Christian Micheloni, Dong Wang, Fei Wang, Fei Xie, Felix Järemo Lawin, Fredrik Gustafsson, Gian Luca Foresti, Goutam Bhat, Guangqi Chen, Haibin Ling, Haitao Zhang, Hakan Cevikalp, Haojie Zhao, Haoran Bai, Hari Chandana Kuchibhotla, Hasan Saribas, Heng Fan, Hossein Ghanei-Yakhdan, Houqiang Li, Houwen Peng, Huchuan Lu, Hui Li, Javad Khaghani, Jesús Bescós, Jianhua Li, Jianlong Fu, Jiaqian Yu, Jingtao Xu, Josef Kittler, Jun Yin, Junhyun Lee, Kaicheng Yu, Kaiwen Liu, Kang Yang, Kenan Dai, Li Cheng, Li Zhang, Lijun Wang, Linyuan Wang, Luc Van Gool, Luca Bertinetto, Matteo Dunnhofer, Miao Cheng, Mohana Murali Dasari, Ning Wang, Pengyu Zhang, Philip H. S. Torr, Qiang Wang, Radu Timofte, Rama Krishna Sai Subrahmanyam Gorthi, Seokeon Choi, Seyed Mojtaba Marvasti-Zadeh, Shao-Chuan Zhao, Shohreh Kasaei, Shoumeng Qiu, Shuhao Chen, Thomas B. Schön, Tianyang Xu, Wei Lu, Weiming Hu, Wengang Zhou, Xi Qiu, Xiao Ke, Xiao-Jun Wu, Xiaolin Zhang, Xiaoyun Yang, Xuefeng Zhu, Yingjie Jiang, Yingming Wang, Yiwei Chen, Yu Ye, Yuezhou Li, Yuncon Yao, Yunsung Lee, Yuzhang Gu, Zezhou Wang, Zhangyong Tang, Zhen-Hua Feng, Zhijun Mai, Zhipeng Zhang, Zhirong Wu, Ziang Ma:
The Eighth Visual Object Tracking VOT2020 Challenge Results. ECCV Workshops (5) 2020: 547-601 - [c60]Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön, Fredrik Lindsten:
Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood. ICASSP 2020: 5860-5864 - [c59]Jack Umenberger, Thomas B. Schön:
Optimistic robust linear quadratic dual control. L4DC 2020: 550-560 - [c58]Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön:
Learning a Deformable Registration Pyramid. MICCAI (Challenges) 2020: 80-86 - [i61]Johannes N. Hendriks, Carl Jidling, Adrian Wills, Thomas B. Schön:
Linearly Constrained Neural Networks. CoRR abs/2002.01600 (2020) - [i60]Jarrad Courts, Christopher Renton, Thomas B. Schön, Adrian Wills:
Constructing a variational family for nonlinear state-space models. CoRR abs/2002.02620 (2020) - [i59]Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön:
The Elliptical Processes: a New Family of Flexible Stochastic Processes. CoRR abs/2003.07201 (2020) - [i58]Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön:
Registration by tracking for sequential 2D MRI. CoRR abs/2003.10819 (2020) - [i57]Daniel Gedon, Niklas Wahlström, Thomas B. Schön, Lennart Ljung:
Deep State Space Models for Nonlinear System Identification. CoRR abs/2003.14162 (2020) - [i56]Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön:
How to Train Your Energy-Based Model for Regression. CoRR abs/2005.01698 (2020) - [i55]Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro, Adrian G. Wills, Thomas B. Schön:
Deep Energy-Based NARX Models. CoRR abs/2012.04136 (2020) - [i54]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Accurate 3D Object Detection using Energy-Based Models. CoRR abs/2012.04634 (2020) - [i53]Jarrad Courts, Adrian Wills, Thomas B. Schön, Brett Ninness:
Variational Nonlinear System Identification. CoRR abs/2012.05072 (2020) - [i52]Antônio H. Ribeiro, Johannes N. Hendriks, Adrian G. Wills, Thomas B. Schön:
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics. CoRR abs/2012.06341 (2020) - [i51]Jarrad Courts, Johannes N. Hendriks, Adrian Wills, Thomas B. Schön, Brett Ninness:
Variational State and Parameter Estimation. CoRR abs/2012.07269 (2020)
2010 – 2019
- 2019
- [j36]Andreas Lindholm, Dave Zachariah, Petre Stoica, Thomas B. Schön:
Data Consistency Approach to Model Validation. IEEE Access 7: 59788-59796 (2019) - [j35]Hildo Bijl, Thomas B. Schön:
Optimal controller/observer gains of discounted-cost LQG systems. Autom. 101: 471-474 (2019) - [j34]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Elements of Sequential Monte Carlo. Found. Trends Mach. Learn. 12(3): 307-392 (2019) - [j33]Patricio E. Valenzuela, Thomas B. Schön, Cristian R. Rojas:
On model order priors for Bayesian identification of SISO linear systems. Int. J. Control 92(7): 1645-1661 (2019) - [j32]Manon Kok, Thomas B. Schön:
A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors. IEEE Signal Process. Lett. 26(11): 1673-1677 (2019) - [j31]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
High-Dimensional Filtering Using Nested Sequential Monte Carlo. IEEE Trans. Signal Process. 67(16): 4177-4188 (2019) - [c57]Jalil Taghia, Thomas B. Schön:
Conditionally Independent Multiresolution Gaussian Processes. AISTATS 2019: 964-973 - [c56]Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön:
Evaluating model calibration in classification. AISTATS 2019: 3459-3467 - [c55]Carl R. Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön:
Deep Convolutional Networks in System Identification. CDC 2019: 3670-3676 - [c54]Jack Umenberger, Thomas B. Schön, Fredrik Lindsten:
Bayesian identification of state-space models via adaptive thermostats. CDC 2019: 7382-7388 - [c53]Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding. ICML 2019: 4942-4950 - [c52]Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson:
Robust exploration in linear quadratic reinforcement learning. NeurIPS 2019: 15310-15320 - [c51]Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist, Thomas B. Schön:
Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter with Delayed Sampling. UAI 2019: 679-689 - [i50]Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding. CoRR abs/1901.09919 (2019) - [i49]Jalil Taghia, Maria Bånkestad, Fredrik Lindsten, Thomas B. Schön:
Constructing the Matrix Multilayer Perceptron and its Application to the VAE. CoRR abs/1902.01182 (2019) - [i48]Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön:
Evaluating model calibration in classification. CoRR abs/1902.06977 (2019) - [i47]Jack Umenberger, Thomas B. Schön:
Nonlinear input design as optimal control of a Hamiltonian system. CoRR abs/1903.02250 (2019) - [i46]Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Elements of Sequential Monte Carlo. CoRR abs/1903.04797 (2019) - [i45]Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. de Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
Automatic Diagnosis of the Short-Duration 12-Lead ECG using a Deep Neural Network: the CODE Study. CoRR abs/1904.01949 (2019) - [i44]Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis Antonio Aguirre:
On the Smoothness of Nonlinear System Identification. CoRR abs/1905.00820 (2019) - [i43]Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson:
Robust exploration in linear quadratic reinforcement learning. CoRR abs/1906.01584 (2019) - [i42]Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön:
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision. CoRR abs/1906.01620 (2019) - [i41]Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön:
The trade-off between long-term memory and smoothness for recurrent networks. CoRR abs/1906.08482 (2019) - [i40]Adrian Wills, Thomas B. Schön:
Stochastic quasi-Newton with line-search regularization. CoRR abs/1909.01238 (2019) - [i39]Carl R. Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön:
Deep Convolutional Networks in System Identification. CoRR abs/1909.01730 (2019) - [i38]Carl Jidling, Johannes N. Hendriks, Thomas B. Schön, Adrian Wills:
Deep kernel learning for integral measurements. CoRR abs/1909.01844 (2019) - [i37]Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, Thomas B. Schön:
DCTD: Deep Conditional Target Densities for Accurate Regression. CoRR abs/1909.12297 (2019) - [i36]Manon Kok, Thomas B. Schön:
A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors. CoRR abs/1910.00463 (2019) - [i35]Jack Umenberger, Thomas B. Schön:
Optimistic robust linear quadratic dual control. CoRR abs/1912.13143 (2019) - 2018
- [j30]Lawrence M. Murray, Thomas B. Schön:
Automated learning with a probabilistic programming language: Birch. Annu. Rev. Control. 46: 29-43 (2018) - [j29]Jack Umenberger, Johan Wågberg, Ian R. Manchester, Thomas B. Schön:
Maximum likelihood identification of stable linear dynamical systems. Autom. 96: 280-292 (2018) - [j28]Arno Solin, Manon Kok, Niklas Wahlstrom, Thomas B. Schön, Simo Särkkä:
Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes. IEEE Trans. Robotics 34(4): 1112-1127 (2018) - [c50]Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön:
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs. AISTATS 2018: 1037-1046 - [c49]Johan Wågberg, Dave Zachariah, Thomas B. Schön:
Regularized parametric system identification: a decision-theoretic formulation. ACC 2018: 1895-1900 - [c48]Roland Hostettler, Thomas B. Schön:
Auxiliary-Particle-Filter-Based Two-Filter Smoothing for Wiener State-Space Models. FUSION 2018: 1-5 - [c47]Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Learning Localized Spatio-Temporal Models From Streaming Data. ICML 2018: 3924-3932 - [c46]Jack Umenberger, Thomas B. Schön:
Learning convex bounds for linear quadratic control policy synthesis. NeurIPS 2018: 9584-9595 - [i34]Carl R. Andersson, Niklas Wahlström, Thomas B. Schön:
Data-Driven Impulse Response Regularization via Deep Learning. CoRR abs/1801.08383 (2018) - [i33]Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Learning Localized Spatio-Temporal Models From Streaming Data. CoRR abs/1802.03334 (2018) - [i32]Adrian Wills, Thomas B. Schön:
Stochastic quasi-Newton with adaptive step lengths for large-scale problems. CoRR abs/1802.04310 (2018) - [i31]Jack Umenberger, Thomas B. Schön:
Learning convex bounds for linear quadratic control policy synthesis. CoRR abs/1806.00319 (2018) - [i30]Zenith Purisha, Carl Jidling, Niklas Wahlström, Simo Särkkä, Thomas B. Schön:
Probabilistic approach to limited-data computed tomography reconstruction. CoRR abs/1809.03779 (2018) - [i29]Adrian Wills, Carl Jidling, Thomas B. Schön:
A fast quasi-Newton-type method for large-scale stochastic optimisation. CoRR abs/1810.01269 (2018) - [i28]Lawrence M. Murray, Thomas B. Schön:
Automated learning with a probabilistic programming language: Birch. CoRR abs/1810.01539 (2018) - [i27]Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão, Derick M. de Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton Pifano, Wagner Meira Jr., Thomas B. Schön, Antônio Luiz P. Ribeiro:
Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network. CoRR abs/1811.12194 (2018) - [i26]Johannes N. Hendriks, Carl Jidling, Adrian Wills, Thomas B. Schön:
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations. CoRR abs/1812.07319 (2018) - 2017
- [j27]Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas, Thomas B. Schön:
On robust input design for nonlinear dynamical models. Autom. 77: 268-278 (2017) - [j26]Andreas Svensson, Thomas B. Schön:
A flexible state-space model for learning nonlinear dynamical systems. Autom. 80: 189-199 (2017) - [j25]Manon Kok, Jeroen D. Hol, Thomas B. Schön:
Using Inertial Sensors for Position and Orientation Estimation. Found. Trends Signal Process. 11(1-2): 1-153 (2017) - [j24]Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen:
System identification through online sparse Gaussian process regression with input noise. IFAC J. Syst. Control. 2: 1-11 (2017) - [c45]Johan Wågberg, Dave Zachariah, Thomas B. Schön, Petre Stoica:
Prediction Performance After Learning in Gaussian Process Regression. AISTATS 2017: 1264-1272 - [c44]Adrian G. Wills, Thomas B. Schön:
On the construction of probabilistic Newton-type algorithms. CDC 2017: 6499-6504 - [c43]Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön:
Linearly constrained Gaussian processes. NIPS 2017: 1215-1224 - [i25]Thomas B. Schön, Andreas Svensson, Lawrence M. Murray, Fredrik Lindsten:
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. CoRR abs/1703.02419 (2017) - [i24]Dave Zachariah, Petre Stoica, Thomas B. Schön:
Online Learning for Distribution-Free Prediction. CoRR abs/1703.05060 (2017) - [i23]Manon Kok, Jeroen D. Hol, Thomas B. Schön:
Using Inertial Sensors for Position and Orientation Estimation. CoRR abs/1704.06053 (2017) - [i22]Hildo Bijl, Thomas B. Schön:
Optimal controller/observer gains of discounted-cost LQG systems. CoRR abs/1706.01042 (2017) - [i21]Johan Wågberg, Dave Zachariah, Thomas B. Schön:
Regularized parametric system identification: a decision-theoretic formulation. CoRR abs/1710.04009 (2017) - [i20]Andreas Svensson, Fredrik Lindsten, Thomas B. Schön:
Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. CoRR abs/1711.10765 (2017) - [i19]Andreas Svensson, Dave Zachariah, Thomas B. Schön:
How consistent is my model with the data? Information-Theoretic Model Check. CoRR abs/1712.02675 (2017) - 2016
- [j23]Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schön, Michel Verhaegen:
Mean and variance of the LQG cost function. Autom. 67: 216-223 (2016) - [j22]Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas B. Schön, Simon J. Godsill:
Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models. IEEE J. Sel. Top. Signal Process. 10(2): 353-365 (2016) - [j21]Liang Dai, Thomas B. Schön:
Using Convolution to Estimate the Score Function for Intractable State-Transition Models. IEEE Signal Process. Lett. 23(4): 498-501 (2016) - [c42]Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön:
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models. AISTATS 2016: 213-221 - [c41]