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Philipp Hennig
Philipp Hennig
University of Tübingen & Max Planck Institute for Intelligent Systems
Verified email at tue.mpg.de - Homepage
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
6392012
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
5182017
Batch Bayesian optimization via local penalization
J González, Z Dai, P Hennig, N Lawrence
Artificial intelligence and statistics, 648-657, 2016
3102016
Probabilistic numerics and uncertainty in computations
P Hennig, MA Osborne, M Girolami
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015
2502015
The randomized dependence coefficient
D Lopez-Paz, P Hennig, B Schölkopf
Advances in Neural Information Processing Systems (NeurIPS) 26, 2013
2092013
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
1992018
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
1812019
Probabilistic line searches for stochastic optimization
M Mahsereci, P Hennig
Advances in Neural Information Processing Systems (NeurIPS) 28, 2015
1362015
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
1352016
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
1282020
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
1182017
Quasi-Newton methods: a new direction.
P Hennig, M Kiefel
J. Mach. Learn. Res. 14 (1), 843-865, 2013
1182013
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
1162019
Coupling adaptive batch sizes with learning rates
L Balles, J Romero, P Hennig
Uncertainty in Artificial Intelligence (UAI) 2017, 2016
1082016
Probabilistic ODE solvers with Runge-Kutta means
M Schober, D Duvenaud, P Hennig
Advances in Neural Information Processing Systems (NeurIPS) 27, 2014
1062014
Dissecting adam: The sign, magnitude and variance of stochastic gradients
L Balles, P Hennig
International Conference on Machine Learning, 404-413, 2018
1052018
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
952014
Active learning of linear embeddings for Gaussian processes
R Garnett, MA Osborne, P Hennig
Uncertainty in Artificial Intelligence (UAI) 2014, 2013
932013
Descending through a crowded valley-benchmarking deep learning optimizers
RM Schmidt, F Schneider, P Hennig
International Conference on Machine Learning, 9367-9376, 2021
832021
Topic models
P Hennig, D Stern, T Graepel, R Herbrich
US Patent 8,645,298, 2014
832014
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