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Philipp Hennig
Philipp Hennig
University of Tübingen
Подтвержден адрес электронной почты в домене uni-tuebingen.de - Главная страница
Название
Процитировано
Процитировано
Год
Entropy search for information-efficient global optimization
P Hennig, CJ Schuler
The Journal of Machine Learning Research 13 (1), 1809-1837, 2012
9232012
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
7912017
Batch Bayesian optimization via local penalization
J González, Z Dai, P Hennig, N Lawrence
Artificial intelligence and statistics, 648-657, 2016
4772016
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
4632018
Laplace redux-effortless bayesian deep learning
E Daxberger, A Kristiadi, A Immer, R Eschenhagen, M Bauer, P Hennig
Advances in neural information processing systems 34, 20089-20103, 2021
4092021
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
3842020
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
3332019
Probabilistic numerics and uncertainty in computations
P Hennig, MA Osborne, M Girolami
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015
3292015
The randomized dependence coefficient
D Lopez-Paz, P Hennig, B Schölkopf
Advances in Neural Information Processing Systems (NeurIPS) 26, 2013
2682013
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
2632019
Descending through a crowded valley-benchmarking deep learning optimizers
RM Schmidt, F Schneider, P Hennig
International Conference on Machine Learning, 9367-9376, 2021
2332021
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
2222016
Dissecting adam: The sign, magnitude and variance of stochastic gradients
L Balles, P Hennig
International Conference on Machine Learning, 404-413, 2018
2122018
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
1772017
Probabilistic line searches for stochastic optimization
M Mahsereci, P Hennig
Advances in Neural Information Processing Systems (NeurIPS) 28, 2015
1592015
Coupling adaptive batch sizes with learning rates
L Balles, J Romero, P Hennig
Uncertainty in Artificial Intelligence (UAI) 2017, 2016
1482016
Quasi-Newton methods: A new direction
P Hennig, M Kiefel
The Journal of Machine Learning Research 14 (1), 843-865, 2013
1392013
Active learning of linear embeddings for Gaussian processes
R Garnett, MA Osborne, P Hennig
Uncertainty in Artificial Intelligence (UAI) 2014, 2013
1362013
Early stopping without a validation set
M Mahsereci, L Balles, C Lassner, P Hennig
arXiv preprint arXiv:1703.09580, 2017
1332017
Probabilistic ODE solvers with Runge-Kutta means
M Schober, D Duvenaud, P Hennig
Advances in Neural Information Processing Systems (NeurIPS) 27, 2014
1312014
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