Philipp Hennig
Philipp Hennig
University of Tübingen & Max Planck Institute for Intelligent Systems
Verified email at - Homepage
Cited by
Cited by
Entropy search for information-efficient global optimization
P Hennig, CJ Schuler
The Journal of Machine Learning Research 13 (1), 1809-1837, 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
Batch bayesian optimization via local penalization
J González, Z Dai, P Hennig, N Lawrence
Artificial intelligence and statistics, 648-657, 2016
Probabilistic numerics and uncertainty in computations
P Hennig, MA Osborne, M Girolami
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015
The randomized dependence coefficient
D Lopez-Paz, P Hennig, B Schölkopf
Advances in neural information processing systems, 1-9, 2013
Probabilistic line searches for stochastic optimization
M Mahsereci, P Hennig
The Journal of Machine Learning Research 18 (1), 4262-4320, 2017
Quasi-Newton method: A new direction
P Hennig, M Kiefel
Journal of Machine Learning Research 14 (Mar), 843-865, 2013
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
Probabilistic ODE solvers with Runge-Kutta means
M Schober, DK Duvenaud, P Hennig
Advances in neural information processing systems, 739-747, 2014
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
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
Topic models
P Hennig, D Stern, T Graepel, R Herbrich
US Patent 8,645,298, 2014
Coupling adaptive batch sizes with learning rates
L Balles, J Romero, P Hennig
arXiv preprint arXiv:1612.05086, 2016
Active learning of linear embeddings for Gaussian processes
R Garnett, MA Osborne, P Hennig
arXiv preprint arXiv:1310.6740, 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
Incremental local gaussian regression
F Meier, P Hennig, S Schaal
Advances in Neural Information Processing Systems, 972-980, 2014
Probabilistic interpretation of linear solvers
P Hennig
SIAM Journal on Optimization 25 (1), 234-260, 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
Gaussian probabilities and expectation propagation
JP Cunningham, P Hennig, S Lacoste-Julien
arXiv preprint arXiv:1111.6832, 2011
Kernel topic models
P Hennig, D Stern, R Herbrich, T Graepel
Artificial Intelligence and Statistics, 511-519, 2012
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