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Thomas B. Sch\"on

Identifiers

  • name variant Thomas B. Sch\"on 0.60 · backfill

Papers (74)

  1. A Rigorous, Tractable Measure of Model Complexity stat.ML · 2026 · author #2
  2. How Do Electrocardiogram Models Scale? cs.LG · 2026 · author #6
  3. Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations cs.LG · 2026 · author #4
  4. Simultaneous State Estimation and Online Model Learning in a Soft Robotic System eess.SY · 2026 · author #5
  5. Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes cs.LG · 2025 · author #4
  6. Is Supervised Learning Really That Different from Unsupervised? stat.ML · 2025 · author #2
  7. A neural signed configuration distance function for path planning of picking manipulators cs.RO · 2025 · author #4
  8. Robust exploration in linear quadratic reinforcement learning math.OC · 2019 · author #3
  9. On the smoothness of nonlinear system identification eess.SY · 2019 · author #4
  10. Nonlinear input design as optimal control of a Hamiltonian system cs.SY · 2019 · author #2
  11. Evaluating model calibration in classification cs.LG · 2019 · author #6
  12. Constructing the Matrix Multilayer Perceptron and its Application to the VAE stat.ML · 2019 · author #4
  13. Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding stat.ME · 2019 · author #3
  14. Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network eess.SP · 2018 · author #9
  15. Probabilistic approach to limited-data computed tomography reconstruction cs.CV · 2018 · author #5
  16. Data Consistency Approach to Model Validation stat.ME · 2018 · author #4
  17. Learning convex bounds for linear quadratic control policy synthesis stat.ML · 2018 · author #2
  18. Conditionally Independent Multiresolution Gaussian Processes stat.ML · 2018 · author #2
  19. Probabilistic modelling and reconstruction of strain physics.data-an · 2018 · author #5
  20. Learning Localized Spatio-Temporal Models From Streaming Data stat.ML · 2018 · author #3
  21. Data-Driven Impulse Response Regularization via Deep Learning cs.SY · 2018 · author #3
  22. How consistent is my model with the data? Information-Theoretic Model Check stat.ML · 2017 · author #3
  23. Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations stat.CO · 2017 · author #3
  24. Regularized parametric system identification: a decision-theoretic formulation cs.SY · 2017 · author #3
  25. Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs stat.ML · 2017 · author #5
  26. Optimal controller/observer gains of discounted-cost LQG systems cs.SY · 2017 · author #2
  27. Using Inertial Sensors for Position and Orientation Estimation cs.RO · 2017 · author #3
  28. On the construction of probabilistic Newton-type algorithms stat.ML · 2017 · author #2
  29. Online Learning for Distribution-Free Prediction cs.LG · 2017 · author #3
  30. Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo stat.CO · 2017 · author #1
  31. Linearly constrained Gaussian processes stat.ML · 2017 · author #4
  32. Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution stat.CO · 2017 · author #2
  33. Smoothing with Couplings of Conditional Particle Filters stat.ME · 2017 · author #3
  34. High-dimensional Filtering using Nested Sequential Monte Carlo stat.CO · 2016 · author #3
  35. Prediction performance after learning in Gaussian process regression stat.ML · 2016 · author #3
  36. Coupling of Particle Filters stat.ME · 2016 · author #3
  37. A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization stat.ML · 2016 · author #2
  38. Linear System Identification via EM with Latent Disturbances and Lagrangian Relaxation stat.CO · 2016 · author #4
  39. A Scalable and Distributed Solution to the Inertial Motion Capture Problem cs.SY · 2016 · author #3
  40. A flexible state space model for learning nonlinear dynamical systems stat.CO · 2016 · author #2
  41. Particle-based Gaussian process optimization for input design in nonlinear dynamical models math.OC · 2016 · author #4
  42. Mean and variance of the LQG cost function cs.SY · 2016 · author #3
  43. System Identification through Online Sparse Gaussian Process Regression with Input Noise stat.ML · 2016 · author #2
  44. Magnetometer calibration using inertial sensors cs.SY · 2016 · author #2
  45. Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables stat.CO · 2015 · author #4
  46. Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models stat.CO · 2015 · author #2
  47. Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models cs.AI · 2015 · author #3
  48. Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion stat.CO · 2015 · author #2
  49. Modeling and interpolation of the ambient magnetic field by Gaussian processes cs.RO · 2015 · author #4
  50. Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods stat.CO · 2015 · author #3
  51. Computationally Efficient Bayesian Learning of Gaussian Process State Space Models stat.CO · 2015 · author #4
  52. Rao-Blackwellized particle smoothers for conditionally linear Gaussian models stat.CO · 2015 · author #4
  53. Particle ancestor sampling for near-degenerate or intractable state transition models stat.CO · 2015 · author #4
  54. Sequential Monte Carlo Methods for System Identification stat.CO · 2015 · author #1
  55. Nonlinear state space smoothing using the conditional particle filter stat.CO · 2015 · author #2
  56. Quasi-Newton particle Metropolis-Hastings stat.CO · 2015 · author #3
  57. Newton-based maximum likelihood estimation in nonlinear state space models stat.CO · 2015 · author #3
  58. Nested Sequential Monte Carlo Methods stat.CO · 2015 · author #3
  59. From Pixels to Torques: Policy Learning with Deep Dynamical Models stat.ML · 2015 · author #2
  60. Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo stat.ML · 2015 · author #3
  61. A new structure exploiting derivation of recursive direct weight optimization cs.SY · 2014 · author #2
  62. Learning deep dynamical models from image pixels stat.ML · 2014 · author #2
  63. Identification of jump Markov linear models using particle filters stat.CO · 2014 · author #2
  64. Divide-and-Conquer with Sequential Monte Carlo stat.CO · 2014 · author #5
  65. Capacity estimation of two-dimensional channels using Sequential Monte Carlo cs.IT · 2014 · author #3
  66. A graph/particle-based method for experiment design in nonlinear systems math.OC · 2014 · author #4
  67. Sequential Monte Carlo for Graphical Models stat.ME · 2014 · author #3
  68. Particle Gibbs with Ancestor Sampling stat.CO · 2014 · author #3
  69. Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM stat.ML · 2013 · author #3
  70. Particle Metropolis-Hastings using gradient and Hessian information stat.CO · 2013 · author #3
  71. Inference in Gaussian models with missing data using Equalisation Maximisation stat.CO · 2013 · author #3
  72. Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC stat.ML · 2013 · author #3
  73. Ancestor Sampling for Particle Gibbs stat.CO · 2012 · author #3
  74. On the use of backward simulation in particle Markov chain Monte Carlo methods stat.CO · 2011 · author #2

Mentions

  • 1511.05483 #4 · backfill · confidence 0.70 Thomas B. Sch\"on
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  • 2505.11006 #2 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on
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  • 1905.00820 #4 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on
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  • 1801.08383 #3 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on
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  • 1703.02419 #1 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on
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  • 1603.09157 #4 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on
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  • 1503.06058 #1 · backfill · confidence 0.70 Thomas B. Sch\"on
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  • 2502.16205 #4 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on
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  • 2605.21167 #2 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on
  • 2605.17276 #6 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on
  • 2602.14092 #5 · arxiv_oai · confidence 0.70 Thomas B. Sch\"on

Frequent Coauthors