Philipp Hennig

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Lukas BallesAmazon ResearchVerified email at amazon.com
Michael Tiemann (né Schober)Research scientist, Bosch Center for Artificial IntelligenceVerified email at de.bosch.com
Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceVerified email at google.com
Christian J. SchulerAlumnus, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Mark BangertGerman Cancer Research Center DKFZVerified email at dkfz.de
Sebastian TrimpeProfessor, RWTH Aachen UniversityVerified email at dsme.rwth-aachen.de
Alonso MarcoIntelligent Control Systems, Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Thore GraepelGlobal Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCLVerified email at ucl.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 KleinAWS Research BerlinVerified email at cs.uni-freiburg.de
Agustinus KristiadiUniversity of TübingenVerified email at uni-tuebingen.de
Maren MahsereciAmazon ResearchVerified email at amazon.com
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Søren HaubergCognitive Systems, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
Hans KerstingPostdoc, SIERRA Team, INRIA ParisVerified email at inria.fr
Matthias HeinProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Edgar D. KlenskeDr. Edgar KlenskeVerified email at klenske.net
Ralf HerbrichZalandoVerified email at herbrich.me

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 Journal of Machine Learning Research 13 (6), 2012	539	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	416	2017
Batch Bayesian optimization via local penalization J González, Z Dai, P Hennig, N Lawrence Artificial intelligence and statistics, 648-657, 2016	245	2016
Probabilistic numerics and uncertainty in computations P Hennig, MA Osborne, M Girolami Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015	220	2015
The randomized dependence coefficient D Lopez-Paz, P Hennig, B Schölkopf Advances in Neural Information Processing Systems (NeurIPS) 26, 2013	186	2013
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	130	2018
Probabilistic line searches for stochastic optimization M Mahsereci, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 28, 2015	122	2015
Dense connectomic reconstruction in layer 4 of the somatosensory cortex A Motta, M Berning, KM Boergens, B Staffler, M Beining, S Loomba, ... Science 366 (6469), 2019	113	2019
Quasi-Newton methods: A new direction P Hennig, M Kiefel The Journal of Machine Learning Research 14 (1), 843-865, 2013	109	2013
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	108	2016
Probabilistic ODE solvers with Runge-Kutta means M Schober, D Duvenaud, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 27, 2014	102	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	98	2017
Coupling adaptive batch sizes with learning rates L Balles, J Romero, P Hennig Uncertainty in Artificial Intelligence (UAI) 2017, 2016	95	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 (NeurIPS) 27, 2014	88	2014
Limitations of the empirical fisher approximation for natural gradient descent F Kunstner, L Balles, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 32, 2019	87	2019
Active learning of linear embeddings for Gaussian processes R Garnett, MA Osborne, P Hennig Uncertainty in Artificial Intelligence (UAI) 2014, 2013	80	2013
Topic models P Hennig, D Stern, T Graepel, R Herbrich US Patent 8,645,298, 2014	69	2014
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	68	2015
Inference of cause and effect with unsupervised inverse regression E Sgouritsa, D Janzing, P Hennig, B Schölkopf Artificial intelligence and statistics, 847-855, 2015	66	2015
Dissecting adam: The sign, magnitude and variance of stochastic gradients L Balles, P Hennig International Conference on Machine Learning, 404-413, 2018	65	2018

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