Philipp Hennig - Google Scholar

Cited by

	All	Since 2015
Citations	2660	2488
h-index	27	26
i10-index	46	43

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525

20112012201320142015201620172018201920207 15 32 95 147 240 389 505 691 502

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Stefan SchaalComputer Scientist in Robotics and AIVerified email at google.com
Lukas BallesPhD Student, University of Tuebingen and MPI for Intelligent SystemsVerified email at tuebingen.mpg.de
Mark BangertGerman Cancer Research Center DKFZVerified email at dkfz.de
Michael Tiemann (née Schober)Research scientist, Bosch Center for Artificial IntelligenceVerified email at de.bosch.com
Christian J. SchulerAlumnus, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Sebastian TrimpeMax Planck Research Group Leader, Max Planck Institute for Intelligent SystemsVerified email at is.mpg.de
Alonso MarcoIntelligent Control Systems, Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Thore GraepelResearch Scientist, Google DeepMind, and Professor of Computer Science, UCLVerified email at ucl.ac.uk
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Stefan FalknerBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Aaron KleinAmazon Research BerlinVerified email at cs.uni-freiburg.de
Søren HaubergCognitive Systems, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
Maren MahsereciAmazon ResearchVerified email at amazon.com
Hans KerstingPhD Student, Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Edgar D. KlenskeBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Roman GarnettWashington University in St. LouisVerified email at wustl.edu
Ralf HerbrichZalandoVerified email at zalando.de
Hans-Peter WieserDKFZ - German Cancer Research CenterVerified email at dkfz-heidelberg.de

Philipp Hennig

University of Tübingen & Max Planck Institute for Intelligent Systems

Verified email at tue.mpg.de - Homepage

Probabilistic Numerics Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Entropy search for information-efficient global optimization P Hennig, CJ Schuler The Journal of Machine Learning Research 13 (1), 1809-1837, 2012	364	2012
Fast bayesian optimization of machine learning hyperparameters on large datasets A Klein, S Falkner, S Bartels, P Hennig, F Hutter Artificial Intelligence and Statistics, 528-536, 2017	221	2017
Batch bayesian optimization via local penalization J González, Z Dai, P Hennig, N Lawrence Artificial intelligence and statistics, 648-657, 2016	163	2016
Probabilistic numerics and uncertainty in computations P Hennig, MA Osborne, M Girolami Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015	146	2015
The randomized dependence coefficient D Lopez-Paz, P Hennig, B Schölkopf Advances in neural information processing systems, 1-9, 2013	140	2013
Quasi-Newton method: A new direction P Hennig, M Kiefel Journal of Machine Learning Research 14 (Mar), 843-865, 2013	87	2013
Probabilistic line searches for stochastic optimization M Mahsereci, P Hennig The Journal of Machine Learning Research 18 (1), 4262-4320, 2017	81	2017
Probabilistic ODE solvers with Runge-Kutta means M Schober, DK Duvenaud, P Hennig Advances in neural information processing systems, 739-747, 2014	74	2014
Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization A Marco, F Berkenkamp, P Hennig, AP Schoellig, A Krause, S Schaal, ... 2017 IEEE International Conference on Robotics and Automation (ICRA), 1557-1563, 2017	72	2017
Automatic LQR tuning based on Gaussian process global optimization A Marco, P Hennig, J Bohg, S Schaal, S Trimpe 2016 IEEE international conference on robotics and automation (ICRA), 270-277, 2016	68	2016
Sampling for inference in probabilistic models with fast Bayesian quadrature T Gunter, MA Osborne, R Garnett, P Hennig, SJ Roberts Advances in neural information processing systems, 2789-2797, 2014	65	2014
Topic models P Hennig, D Stern, T Graepel, R Herbrich US Patent 8,645,298, 2014	58	2014
Coupling adaptive batch sizes with learning rates L Balles, J Romero, P Hennig arXiv preprint arXiv:1612.05086, 2016	56	2016
Incremental local gaussian regression F Meier, P Hennig, S Schaal Advances in Neural Information Processing Systems, 972-980, 2014	51	2014
Probabilistic interpretation of linear solvers P Hennig SIAM Journal on Optimization 25 (1), 234-260, 2015	50	2015
Active learning of linear embeddings for Gaussian processes R Garnett, MA Osborne, P Hennig arXiv preprint arXiv:1310.6740, 2013	49	2013
Inference of cause and effect with unsupervised inverse regression E Sgouritsa, D Janzing, P Hennig, B Schölkopf Artificial intelligence and statistics, 847-855, 2015	47	2015
Gaussian processes and kernel methods: A review on connections and equivalences M Kanagawa, P Hennig, D Sejdinovic, BK Sriperumbudur arXiv preprint arXiv:1807.02582, 2018	46	2018
Kernel topic models P Hennig, D Stern, R Herbrich, T Graepel Artificial Intelligence and Statistics, 511-519, 2012	44	2012
Gaussian process-based predictive control for periodic error correction ED Klenske, MN Zeilinger, B Schölkopf, P Hennig IEEE Transactions on Control Systems Technology 24 (1), 110-121, 2015	43	2015

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