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
Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceVerified email at google.com
Michael Tiemann (né 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
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 GraepelResearch Scientist, Google DeepMind, and Professor of Computer Science, 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
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
Maren MahsereciAmazon ResearchVerified email at amazon.com
Hans KerstingSIERRA Team, INRIA ParisVerified email at inria.fr
Ralf HerbrichZalandoVerified email at herbrich.me
Edgar D. KlenskeDr. Edgar KlenskeVerified email at klenske.net
Roman GarnettWashington University in St. LouisVerified email at wustl.edu
Agustinus KristiadiUniversity of TübingenVerified email at uni-tuebingen.de

Philipp Hennig

University of Tübingen & Max Planck Institute for Intelligent Systems

Verified email at tue.mpg.de - Homepage

Probabilistic Numerics Machine Learning


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Entropy Search for Information-Efficient Global Optimization. P Hennig, CJ Schuler Journal of Machine Learning Research 13 (6), 2012	504	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	371	2017
Batch Bayesian optimization via local penalization J González, Z Dai, P Hennig, N Lawrence Artificial intelligence and statistics, 648-657, 2016	226	2016
Probabilistic numerics and uncertainty in computations P Hennig, MA Osborne, M Girolami Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015	204	2015
The randomized dependence coefficient D Lopez-Paz, P Hennig, B Schölkopf Advances in Neural Information Processing Systems (NeurIPS) 26, 2013	181	2013
Probabilistic line searches for stochastic optimization M Mahsereci, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 28, 2015	119	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	112	2018
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	104	2019
Quasi-Newton methods: A new direction P Hennig, M Kiefel The Journal of Machine Learning Research 14 (1), 843-865, 2013	103	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	101	2016
Probabilistic ODE solvers with Runge-Kutta means M Schober, D Duvenaud, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 27, 2014	95	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	91	2017
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	86	2014
Coupling adaptive batch sizes with learning rates L Balles, J Romero, P Hennig Uncertainty in Artificial Intelligence (UAI) 2017, 2016	80	2016
Active learning of linear embeddings for Gaussian processes R Garnett, MA Osborne, P Hennig Uncertainty in Artificial Intelligence (UAI) 2014, 2013	76	2013
Topic models P Hennig, D Stern, T Graepel, R Herbrich US Patent 8,645,298, 2014	69	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	66	2019
Probabilistic interpretation of linear solvers P Hennig SIAM Journal on Optimization 25 (1), 234-260, 2015	64	2015
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	62	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	61	2015

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