| Entropy search for information-efficient global optimization P Hennig, CJ Schuler The Journal of Machine Learning Research 13 (1), 1809-1837, 2012 | 1014 | 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 | 855 | 2017 |
| 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 | 541 | 2018 |
| Batch Bayesian optimization via local penalization J González, Z Dai, P Hennig, N Lawrence Artificial intelligence and statistics, 648-657, 2016 | 530 | 2016 |
| 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 | 519 | 2021 |
| 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 | 429 | 2020 |
| 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 | 360 | 2019 |
| Probabilistic numerics and uncertainty in computations P Hennig, MA Osborne, M Girolami Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015 | 354 | 2015 |
| 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 | 300 | 2019 |
| The randomized dependence coefficient D Lopez-Paz, P Hennig, B Schölkopf Advances in Neural Information Processing Systems (NeurIPS) 26, 2013 | 294 | 2013 |
| Descending through a crowded valley-benchmarking deep learning optimizers RM Schmidt, F Schneider, P Hennig International Conference on Machine Learning, 9367-9376, 2021 | 276 | 2021 |
| Dissecting adam: The sign, magnitude and variance of stochastic gradients L Balles, P Hennig International Conference on Machine Learning, 404-413, 2018 | 250 | 2018 |
| 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 | 248 | 2016 |
| 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 | 191 | 2017 |
| Probabilistic line searches for stochastic optimization M Mahsereci, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 28, 2015 | 167 | 2015 |
| Probabilistic Numerics: Computation as Machine Learning P Hennig, MA Osborne, HP Kersting Cambridge University Press, 2022 | 161 | 2022 |
| Coupling adaptive batch sizes with learning rates L Balles, J Romero, P Hennig Uncertainty in Artificial Intelligence (UAI) 2017, 2016 | 160 | 2016 |
| Early stopping without a validation set M Mahsereci, L Balles, C Lassner, P Hennig arXiv preprint arXiv:1703.09580, 2017 | 155 | 2017 |
| Active learning of linear embeddings for Gaussian processes R Garnett, MA Osborne, P Hennig Uncertainty in Artificial Intelligence (UAI) 2014, 2013 | 146 | 2013 |
| Probabilistic ODE solvers with Runge-Kutta means M Schober, D Duvenaud, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 27, 2014 | 145 | 2014 |
Agustinus KristiadiAssistant Professor, Western UniversityVerified email at uwo.ca
