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ORCIDPhilipp Hennig
Person information
- affiliation: University of Tübingen, Department of Computer Science, Germany
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- affiliation (PhD 2011): University of Cambridge, UK
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2020 – today
- 2023
[j15]Felix Dangel, Lukas Tatzel, Philipp Hennig:
ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure. Trans. Mach. Learn. Res. 2023 (2023)- [i84]Agustinus Kristiadi, Felix Dangel, Philipp Hennig:
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. CoRR abs/2302.07384 (2023) - [i83]Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol:
Bayesian Numerical Integration with Neural Networks. CoRR abs/2305.13248 (2023) - [i82]Katharina Ott, Michael Tiemann, Philipp Hennig:
Uncertainty and Structure in Neural Ordinary Differential Equations. CoRR abs/2305.13290 (2023) - [i81]Nathanael Bosch, Philipp Hennig, Filip Tronarp:
Probabilistic Exponential Integrators. CoRR abs/2305.14978 (2023) - 2022
- [j14]Jonathan Oesterle
, Nicholas Krämer, Philipp Hennig, Philipp Berens:
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J. Comput. Neurosci. 50(4): 485-503 (2022) - [c66]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being a Bit Frequentist Improves Bayesian Neural Networks. AISTATS 2022: 529-545 - [c65]Nicholas Krämer, Jonathan Schmidt, Philipp Hennig:
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. AISTATS 2022: 625-639 - [c64]Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg:
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. AISTATS 2022: 1503-1526 - [c63]Nathanael Bosch, Filip Tronarp, Philipp Hennig:
Pick-and-Mix Information Operators for Probabilistic ODE Solvers. AISTATS 2022: 10015-10027 - [c62]Matthias Werner, Andrej Junginger, Philipp Hennig, Georg Martius:
Uncertainty in equation learning. GECCO Companion 2022: 2298-2305 - [c61]Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig:
Probabilistic ODE Solutions in Millions of Dimensions. ICML 2022: 11634-11649 - [c60]Filip Tronarp, Nathanael Bosch, Philipp Hennig:
Fenrir: Physics-Enhanced Regression for Initial Value Problems. ICML 2022: 21776-21794 - [c59]Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner:
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML 2022: 23751-23780 - [c58]Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig:
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. NeurIPS 2022 - [c57]Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham:
Posterior and Computational Uncertainty in Gaussian Processes. NeurIPS 2022 - [c56]Fynn Bachmann, Philipp Hennig, Dmitry Kobak:
Wasserstein t-SNE. ECML/PKDD (1) 2022: 104-120 - [c55]Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig:
Fast predictive uncertainty for classification with Bayesian deep networks. UAI 2022: 822-832 - [i80]Filip Tronarp, Nathanael Bosch, Philipp Hennig:
Fenrir: Physics-Enhanced Regression for Initial Value Problems. CoRR abs/2202.01287 (2022) - [i79]Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg:
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. CoRR abs/2203.03353 (2022) - [i78]Fynn Bachmann, Philipp Hennig, Dmitry Kobak:
Wasserstein t-SNE. CoRR abs/2205.07531 (2022) - [i77]Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig:
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. CoRR abs/2205.10041 (2022) - [i76]Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham:
Posterior and Computational Uncertainty in Gaussian Processes. CoRR abs/2205.15449 (2022) - [i75]Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig:
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/2208.01565 (2022) - [i74]Julia Grosse, Cheng Zhang, Philipp Hennig:
Optimistic Optimization of Gaussian Process Samples. CoRR abs/2209.00895 (2022) - [i73]Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger:
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. CoRR abs/2212.12474 (2022) - 2021
- [j13]Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, Sebastian Trimpe:
Robot Learning With Crash Constraints. IEEE Robotics Autom. Lett. 6(2): 1439-1446 (2021) - [j12]Filip Tronarp, Simo Särkkä, Philipp Hennig:
Bayesian ODE solvers: the maximum a posteriori estimate. Stat. Comput. 31(3): 23 (2021) - [c54]Nathanael Bosch, Philipp Hennig, Filip Tronarp:
Calibrated Adaptive Probabilistic ODE Solvers. AISTATS 2021: 3466-3474 - [c53]Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann:
ResNet After All: Neural ODEs and Their Numerical Solution. ICLR 2021 - [c52]Filip de Roos, Alexandra Gessner, Philipp Hennig:
High-Dimensional Gaussian Process Inference with Derivatives. ICML 2021: 2535-2545 - [c51]Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis:
Bayesian Quadrature on Riemannian Data Manifolds. ICML 2021: 3459-3468 - [c50]Robin M. Schmidt, Frank Schneider, Philipp Hennig:
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. ICML 2021: 9367-9376 - [c49]Nicholas Krämer, Philipp Hennig:
Linear-Time Probabilistic Solution of Boundary Value Problems. NeurIPS 2021: 11160-11171 - [c48]Jonathan Schmidt, Nicholas Krämer, Philipp Hennig:
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. NeurIPS 2021: 12374-12385 - [c47]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. NeurIPS 2021: 18789-18800 - [c46]Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS 2021: 20089-20103 - [c45]Frank Schneider, Felix Dangel, Philipp Hennig:
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks. NeurIPS 2021: 20825-20837 - [c44]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Learnable uncertainty under Laplace approximations. UAI 2021: 344-353 - [c43]Julia Grosse, Cheng Zhang, Philipp Hennig:
Probabilistic DAG search. UAI 2021: 1424-1433 - [i72]Frank Schneider, Felix Dangel, Philipp Hennig:
Cockpit: A Practical Debugging Tool for Training Deep Neural Networks. CoRR abs/2102.06604 (2021) - [i71]Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis:
Bayesian Quadrature on Riemannian Data Manifolds. CoRR abs/2102.06645 (2021) - [i70]Filip de Roos, Alexandra Gessner, Philipp Hennig:
High-Dimensional Gaussian Process Inference with Derivatives. CoRR abs/2102.07542 (2021) - [i69]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) - [i68]Jonathan Schmidt, Nicholas Krämer, Philipp Hennig:
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. CoRR abs/2103.10153 (2021) - [i67]Marius Hobbhahn, Philipp Hennig:
Laplace Matching for fast Approximate Inference in Generalized Linear Models. CoRR abs/2105.03109 (2021) - [i66]Matthias Werner, Andrej Junginger, Philipp Hennig, Georg Martius:
Informed Equation Learning. CoRR abs/2105.06331 (2021) - [i65]Felix Dangel, Lukas Tatzel, Philipp Hennig:
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure. CoRR abs/2106.02624 (2021) - [i64]Nicholas Krämer, Philipp Hennig:
Linear-Time Probabilistic Solutions of Boundary Value Problems. CoRR abs/2106.07761 (2021) - [i63]Julia Grosse, Cheng Zhang, Philipp Hennig:
Probabilistic DAG Search. CoRR abs/2106.08717 (2021) - [i62]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being a Bit Frequentist Improves Bayesian Neural Networks. CoRR abs/2106.10065 (2021) - [i61]Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. CoRR abs/2106.14806 (2021) - [i60]Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner:
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR abs/2107.00243 (2021) - [i59]Nathanael Bosch, Filip Tronarp, Philipp Hennig:
Pick-and-Mix Information Operators for Probabilistic ODE Solvers. CoRR abs/2110.10770 (2021) - [i58]Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig:
Probabilistic ODE Solutions in Millions of Dimensions. CoRR abs/2110.11812 (2021) - [i57]Nicholas Krämer, Jonathan Schmidt, Philipp Hennig:
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. CoRR abs/2110.11847 (2021) - [i56]Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi:
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. CoRR abs/2111.03577 (2021) - [i55]Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig:
ProbNum: Probabilistic Numerics in Python. CoRR abs/2112.02100 (2021) - [i54]Philipp Hennig, Ilse C. F. Ipsen, Maren Mahsereci, Tim Sullivan:
Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432). Dagstuhl Reports 11(9): 102-119 (2021) - 2020
- [j11]Simon Bartels, Philipp Hennig:
Conjugate Gradients for Kernel Machines. J. Mach. Learn. Res. 21: 55:1-55:42 (2020) - [j10]Hans Kersting, Timothy John Sullivan, Philipp Hennig:
Convergence rates of Gaussian ODE filters. Stat. Comput. 30(6): 1791-1816 (2020) - [c42]Felix Dangel, Stefan Harmeling, Philipp Hennig:
Modular Block-diagonal Curvature Approximations for Feedforward Architectures. AISTATS 2020: 799-808 - [c41]Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig:
Integrals over Gaussians under Linear Domain Constraints. AISTATS 2020: 2764-2774 - [c40]Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS 2020: 60-69 - [c39]Felix Dangel, Frederik Kunstner, Philipp Hennig:
BackPACK: Packing more into Backprop. ICLR 2020 - [c38]Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig:
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. ICML 2020: 5198-5208 - [c37]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. ICML 2020: 5436-5446 - [c36]Jonathan Wenger, Philipp Hennig:
Probabilistic Linear Solvers for Machine Learning. NeurIPS 2020 - [i53]Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig:
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. CoRR abs/2002.09301 (2020) - [i52]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. CoRR abs/2002.10118 (2020) - [i51]Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig:
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks. CoRR abs/2003.01227 (2020) - [i50]Filip Tronarp, Simo Särkkä, Philipp Hennig:
Bayesian ODE Solvers: The Maximum A Posteriori Estimate. CoRR abs/2004.00623 (2020) - [i49]Robin M. Schmidt, Frank Schneider, Philipp Hennig:
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. CoRR abs/2007.01547 (2020) - [i48]Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann:
When are Neural ODE Solutions Proper ODEs? CoRR abs/2007.15386 (2020) - [i47]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. CoRR abs/2010.02709 (2020) - [i46]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Learnable Uncertainty under Laplace Approximations. CoRR abs/2010.02720 (2020) - [i45]Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, Sebastian Trimpe:
Robot Learning with Crash Constraints. CoRR abs/2010.08669 (2020) - [i44]Jonathan Wenger, Philipp Hennig:
Probabilistic Linear Solvers for Machine Learning. CoRR abs/2010.09691 (2020) - [i43]Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR abs/2011.04803 (2020) - [i42]Nathanael Bosch, Philipp Hennig, Filip Tronarp:
Calibrated Adaptive Probabilistic ODE Solvers. CoRR abs/2012.08202 (2020) - [i41]Nicholas Krämer, Philipp Hennig:
Stable Implementation of Probabilistic ODE Solvers. CoRR abs/2012.10106 (2020)
2010 – 2019
- 2019
- [j9]Michael Schober, Simo Särkkä, Philipp Hennig:
A probabilistic model for the numerical solution of initial value problems. Stat. Comput. 29(1): 99-122 (2019) - [j8]Simon Bartels, Jon Cockayne, Ilse C. F. Ipsen, Philipp Hennig:
Probabilistic linear solvers: a unifying view. Stat. Comput. 29(6): 1249-1263 (2019) - [j7]Filip Tronarp, Hans Kersting, Simo Särkkä, Philipp Hennig:
Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective. Stat. Comput. 29(6): 1297-1315 (2019) - [c35]Filip de Roos, Philipp Hennig:
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization. AISTATS 2019: 1448-1457 - [c34]Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober:
Fast and Robust Shortest Paths on Manifolds Learned from Data. AISTATS 2019: 1506-1515 - [c33]Frank Schneider, Lukas Balles, Philipp Hennig:
DeepOBS: A Deep Learning Optimizer Benchmark Suite. ICLR (Poster) 2019 - [c32]Frederik Kunstner, Philipp Hennig, Lukas Balles:
Limitations of the empirical Fisher approximation for natural gradient descent. NeurIPS 2019: 4158-4169 - [c31]Motonobu Kanagawa, Philipp Hennig:
Convergence Guarantees for Adaptive Bayesian Quadrature Methods. NeurIPS 2019: 6234-6245 - [i40]Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober:
Fast and Robust Shortest Paths on Manifolds Learned from Data. CoRR abs/1901.07229 (2019) - [i39]Felix Dangel, Philipp Hennig:
A Modular Approach to Block-diagonal Hessian Approximations for Second-order Optimization Methods. CoRR abs/1902.01813 (2019) - [i38]Filip de Roos, Philipp Hennig:
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization. CoRR abs/1902.07557 (2019) - [i37]Frank Schneider, Lukas Balles, Philipp Hennig:
DeepOBS: A Deep Learning Optimizer Benchmark Suite. CoRR abs/1903.05499 (2019) - [i36]Motonobu Kanagawa, Philipp Hennig:
Convergence Guarantees for Adaptive Bayesian Quadrature Methods. CoRR abs/1905.10271 (2019) - [i35]Frederik Kunstner, Lukas Balles, Philipp Hennig:
Limitations of the Empirical Fisher Approximation. CoRR abs/1905.12558 (2019) - [i34]Michael Lohaus, Philipp Hennig, Ulrike von Luxburg:
Uncertainty Estimates for Ordinal Embeddings. CoRR abs/1906.11655 (2019) - [i33]Alonso Marco, Dominik Baumann, Philipp Hennig, Sebastian Trimpe:
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties. CoRR abs/1907.10383 (2019) - [i32]Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig:
Integrals over Gaussians under Linear Domain Constraints. CoRR abs/1910.09328 (2019) - [i31]Simon Bartels, Philipp Hennig:
Conjugate Gradients for Kernel Machines. CoRR abs/1911.06048 (2019) - [i30]Felix Dangel, Frederik Kunstner, Philipp Hennig:
BackPACK: Packing more into backprop. CoRR abs/1912.10985 (2019) - 2018
- [c30]Lukas Balles, Philipp Hennig:
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. ICML 2018: 413-422 - [i29]Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur:
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. CoRR abs/1807.02582 (2018) - [i28]Hans Kersting, Timothy John Sullivan, Philipp Hennig:
Convergence Rates of Gaussian ODE Filters. CoRR abs/1807.09737 (2018) - 2017
- [j6]Maren Mahsereci, Philipp Hennig:
Probabilistic Line Searches for Stochastic Optimization. J. Mach. Learn. Res. 18: 119:1-119:59 (2017) - [c29]Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. AISTATS 2017: 528-536 - [c28]Alonso Marco, Philipp Hennig, Stefan Schaal, Sebastian Trimpe:
On the design of LQR kernels for efficient controller learning. CDC 2017: 5193-5200 - [c27]Alonso Marco, Felix Berkenkamp, Philipp Hennig, Angela P. Schoellig, Andreas Krause, Stefan Schaal, Sebastian Trimpe:
Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization. ICRA 2017: 1557-1563 - [c26]Lukas Balles, Javier Romero, Philipp Hennig:
Coupling Adaptive Batch Sizes with Learning Rates. UAI 2017 - [i27]Alonso Marco, Felix Berkenkamp, Philipp Hennig, Angela P. Schoellig, Andreas Krause, Stefan Schaal, Sebastian Trimpe:
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization. CoRR abs/1703.01250 (2017) - [i26]Maren Mahsereci, Lukas Balles, Christoph Lassner, Philipp Hennig:
Early Stopping without a Validation Set. CoRR abs/1703.09580 (2017) - [i25]Maren Mahsereci, Philipp Hennig:
Probabilistic Line Searches for Stochastic Optimization. CoRR abs/1703.10034 (2017) - [i24]Lukas Balles, Philipp Hennig:
Follow the Signs for Robust Stochastic Optimization. CoRR abs/1705.07774 (2017) - [i23]Filip de Roos, Philipp Hennig:
Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning. CoRR abs/1706.00241 (2017) - [i22]Paul K. Rubenstein, Ilya O. Tolstikhin, Philipp Hennig, Bernhard Schölkopf:
Probabilistic Active Learning of Functions in Structural Causal Models. CoRR abs/1706.10234 (2017) - [i21]Alonso Marco, Philipp Hennig, Stefan Schaal, Sebastian Trimpe:
On the Design of LQR Kernels for Efficient Controller Learning. CoRR abs/1709.07089 (2017) - [i20]Emilia Magnani, Hans Kersting, Michael Schober, Philipp Hennig:
Bayesian Filtering for ODEs with Bounded Derivatives. CoRR abs/1709.08471 (2017) - 2016
- [j5]Edgar D. Klenske, Philipp Hennig:
Dual Control for Approximate Bayesian Reinforcement Learning. J. Mach. Learn. Res. 17: 127:1-127:30 (2016) - [j4]Edgar D. Klenske, Melanie Nicole Zeilinger, Bernhard Schölkopf, Philipp Hennig:
Gaussian Process-Based Predictive Control for Periodic Error Correction. IEEE Trans. Control. Syst. Technol. 24(1): 110-121 (2016) - [c25]Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence:
Batch Bayesian Optimization via Local Penalization. AISTATS 2016: 648-657 - [c24]Simon Bartels, Philipp Hennig:
Probabilistic Approximate Least-Squares. AISTATS 2016: 676-684 - [c23]Edgar D. Klenske, Philipp Hennig, Bernhard Schölkopf, Melanie Nicole Zeilinger:
Approximate dual control maintaining the value of information with an application to building control. ECC 2016: 800-806 - [c22]Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe:
Automatic LQR tuning based on Gaussian process global optimization. ICRA 2016: 270-277 - [c21]Hans Kersting, Philipp Hennig:
Active Uncertainty Calibration in Bayesian ODE Solvers. UAI 2016 - [i19]Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe:
Automatic LQR Tuning Based on Gaussian Process Global Optimization. CoRR abs/1605.01950 (2016) - [i18]Hans Kersting, Philipp Hennig:
Active Uncertainty Calibration in Bayesian ODE Solvers. CoRR abs/1605.03364 (2016) - [i17]Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. CoRR abs/1605.07079 (2016) - [i16]