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2017


On the Design of {LQR} Kernels for Efficient Controller Learning
On the Design of LQR Kernels for Efficient Controller Learning

Marco, A., Hennig, P., Schaal, S., Trimpe, S.

Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC), pages: 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (conference)

Abstract
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.

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arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page [BibTex]

2017


arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page [BibTex]


Coupling Adaptive Batch Sizes with Learning Rates
Coupling Adaptive Batch Sizes with Learning Rates

Balles, L., Romero, J., Hennig, P.

In Proceedings Conference on Uncertainty in Artificial Intelligence (UAI) 2017, pages: 410-419, (Editors: Gal Elidan and Kristian Kersting), Association for Uncertainty in Artificial Intelligence (AUAI), Conference on Uncertainty in Artificial Intelligence (UAI), August 2017 (inproceedings)

Abstract
Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates. This variance also changes over the optimization process; when using a constant batch size, stability and convergence is thus often enforced by means of a (manually tuned) decreasing learning rate schedule. We propose a practical method for dynamic batch size adaptation. It estimates the variance of the stochastic gradients and adapts the batch size to decrease the variance proportionally to the value of the objective function, removing the need for the aforementioned learning rate decrease. In contrast to recent related work, our algorithm couples the batch size to the learning rate, directly reflecting the known relationship between the two. On three image classification benchmarks, our batch size adaptation yields faster optimization convergence, while simultaneously simplifying learning rate tuning. A TensorFlow implementation is available.

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Code link (url) Project Page [BibTex]

Code link (url) Project Page [BibTex]


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Dynamic Time-of-Flight

Schober, M., Adam, A., Yair, O., Mazor, S., Nowozin, S.

Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 170-179, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (conference)

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DOI [BibTex]

DOI [BibTex]


Virtual vs. {R}eal: Trading Off Simulations and Physical Experiments in Reinforcement Learning with {B}ayesian Optimization
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization

Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 1557-1563, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

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PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]

PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]


Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), 54, pages: 528-536, Proceedings of Machine Learning Research, (Editors: Sign, Aarti and Zhu, Jerry), PMLR, April 2017 (conference)

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pdf link (url) Project Page [BibTex]

pdf link (url) Project Page [BibTex]

2004


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High-speed dynamics of magnetization processes in hard magnetic particles and thin platelets

Goll, D., Kronmüller, H.

In Proceedings of the 18th International Workshop on Rare-Earth Magnets and their Applications, pages: 465-469, Laboratoire de Cristallographie/Laboratoire Louis Neel, CNRS, Grenoble, 2004 (inproceedings)

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[BibTex]

2004


[BibTex]


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Nanoscale Materials for Energy Storage
{Materials Science \& Engineering B}, 108, pages: 292, Elsevier, 2004 (misc)

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[BibTex]

[BibTex]


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High-speed dynamics of magnetization processes in hard magnetic particles and thin platelets

Goll, D., Kronmüller, H.

In Proceedings of the 18th International Workshop on Rare-Earth Magnets and their Applications, pages: 465-469, Laboratoire de Cristallographie/Laboratoire Louis Neel, CNRS, Grenoble, 2004 (inproceedings)

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[BibTex]

[BibTex]


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Modern nanocrystalline/nanostructured hard magnetic materials

Kronmüller, H., Goll, D.

In 272-276, pages: e319-e320, Rome [Italy], 2004 (inproceedings)

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[BibTex]

[BibTex]


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Modern nanostructured high-temperature permanent magnets

Goll, D., Kronmüller, H., Stadelmaier, H. H.

In Proceedings of the 18th International Workshop on Rare-Earth Magnets and their Applications, pages: 578-583, Laboratoire de Cristallographie/Laboratoire Louis Néel, CNRS, Grenoble, 2004 (inproceedings)

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[BibTex]

[BibTex]


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Imaging sub-ns spin dynamics in magnetic nanostructures with magnetic transmission X-ray microscopy

Fischer, P., Stoll, H., Puzic, A., Van Waeyenberge, B., Raabe, J., Haug, T., Denbeaux, G., Pearson, A., Höllinger, R., Back, C. H., Weiss, D., Schütz, G.

In Synchrotron Radiation Instrumentation, 705, pages: 1291-1294, AIP Conference Proceedings, American Institute of Physics, San Francisco, California (USA), 2004 (inproceedings)

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[BibTex]

[BibTex]


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Modern nanostructured high-temperature permanent magnets

Goll, D., Kronmüller, H., Stadelmaier, H. H.

In Proceedings of the 18th International Workshop on Rare-Earth Magnets and their Applications, pages: 578-583, Laboratoire de Cristallographie/Laboratoire Louis Néel, CNRS, Grenoble, 2004 (inproceedings)

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[BibTex]

[BibTex]


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Existence of transient temperature spike induced by SHI: evidence by ion beam analysis

Avasthi, D. K., Ghosh, S., Srivastava, S. K., Assmann, W.

In 219-220, pages: 206-214, Albuquerque, NM [USA], 2004 (inproceedings)

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[BibTex]

[BibTex]


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Hard magnetic hollow nanospheres

Goll, D., Berkowitz, A. E., Bertram, H. N.

In Proceedings of the 18th International Workshop on Rare-Earth Magnets and their Applications, pages: 704-707, Laboratoire de Cristallographie/Laboratoire Louis Neel, CNRS, Grenoble, 2004 (inproceedings)

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[BibTex]

[BibTex]

2000


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High-performance nanocrystalline PrFeB-based bonded permanent magnets

Goll, D., Kleinschroth, I., Kronmüller, H.

In Proceedings of the 16th International Workshop on Rare-Earth Magnets and Their Applications, pages: 641-650, Japan Institute of Metals, 2000 (inproceedings)

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[BibTex]

2000


[BibTex]


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Experimental and theoretical study of the Verwey transition in magnetite

Brabers, V. A. M., Brabers, J. H. V. J., Walz, F., Kronmüller, H.

In Proceedings 8th International Conference on Ferrites, pages: 123-125, Japan Society of Powder and Powder Metallurgy, 2000 (inproceedings)

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[BibTex]

[BibTex]


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Evolution of microstructure and microchemistry in the high-temperature Sm(Co, Fe, Cu, Zr)z magnets

Zhang, Y. W., Hadjipanayis, G. C., Goll, D., Kronmüller, H., Chen, C., Nelson, C., Krishnan, K.

In Proceedings of the 16th International Workshop on Rare-Earth Magnets and Their Applications, pages: 169-178, Sendai, Japan, 2000 (inproceedings)

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[BibTex]

[BibTex]


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Fundamental investigations and industrial applications of magnetostriction

Hirscher, M., Fischer, S. F., Reininger, T.

In Modern Trends in Magnetostriction Study and Application. Proceedings of the NATO Advanced Study Institute on Modern Trends in Magnetostriction, 5, pages: 307-329, NATO Science Series: II: Mathematics, Physics and Chemistry, Kluwer Academic Publishers, Kyiv, Ukraine, 2000 (inproceedings)

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[BibTex]

[BibTex]


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Micromagnetic and microstructural analysis of the temperature dependence of the coercive field of Sm2(Co, Cu, Fe, Zr)17 permanent magnets

Goll, D., Sigle, W., Hadjipanayis, G. C., Kronmüller, H.

In Proceedings of the 16th International Workshop on Rare-Earth Magnets and Their Applications, pages: 61-70, Kaneko, H.; Homma, M.; Okada, M., 2000 (inproceedings)

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[BibTex]

[BibTex]