Philipp Hennig

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Lukas BallesAmazon ResearchVerified email at amazon.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Agustinus KristiadiUniversity of TübingenVerified email at uni-tuebingen.de
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
Sebastian TrimpeProfessor, RWTH Aachen UniversityVerified email at dsme.rwth-aachen.de
Alonso MarcoUniversity of California BerkeleyVerified email at berkeley.edu
Mark BangertGerman Cancer Research Center DKFZVerified email at dkfz.de
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
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 amazon.com
Nicholas KrämerUniversity of TübingenVerified email at uni-tuebingen.de
Hans KerstingPostdoc, SIERRA Team, INRIA ParisVerified email at inria.fr
Maren MahsereciAmazon ResearchVerified email at amazon.com
Søren HaubergCognitive Systems, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
Matthias HeinProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Simo SärkkäAssociate Professor, Aalto UniversityVerified email at aalto.fi

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	663	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	538	2017
Batch Bayesian optimization via local penalization J González, Z Dai, P Hennig, N Lawrence Artificial intelligence and statistics, 648-657, 2016	316	2016
Probabilistic numerics and uncertainty in computations P Hennig, MA Osborne, M Girolami Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015	255	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	216	2018
The randomized dependence coefficient D Lopez-Paz, P Hennig, B Schölkopf Advances in Neural Information Processing Systems (NeurIPS) 26, 2013	212	2013
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), eaay3134, 2019	180	2019
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	139	2016
Probabilistic line searches for stochastic optimization M Mahsereci, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 28, 2015	138	2015
Being bayesian, even just a bit, fixes overconfidence in relu networks A Kristiadi, M Hein, P Hennig International conference on machine learning, 5436-5446, 2020	135	2020
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	122	2017
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	121	2019
Quasi-Newton methods: A new direction P Hennig, M Kiefel The Journal of Machine Learning Research 14 (1), 843-865, 2013	119	2013
Coupling adaptive batch sizes with learning rates L Balles, J Romero, P Hennig Uncertainty in Artificial Intelligence (UAI) 2017, 2016	113	2016
Dissecting adam: The sign, magnitude and variance of stochastic gradients L Balles, P Hennig International Conference on Machine Learning, 404-413, 2018	109	2018
Probabilistic ODE solvers with Runge-Kutta means M Schober, D Duvenaud, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 27, 2014	106	2014
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	98	2014
Active learning of linear embeddings for Gaussian processes R Garnett, MA Osborne, P Hennig Uncertainty in Artificial Intelligence (UAI) 2014, 2013	96	2013
Descending through a crowded valley-benchmarking deep learning optimizers RM Schmidt, F Schneider, P Hennig International Conference on Machine Learning, 9367-9376, 2021	90	2021
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	84	2015

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