Entropy search for information-efficient global optimization. P Hennig, CJ Schuler Journal of Machine Learning Research 13 (6), 2012 | 815 | 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 | 703 | 2017 |
Batch Bayesian optimization via local penalization J González, Z Dai, P Hennig, N Lawrence Artificial intelligence and statistics, 648-657, 2016 | 405 | 2016 |
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 | 354 | 2018 |
Probabilistic numerics and uncertainty in computations P Hennig, MA Osborne, M Girolami Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015 | 312 | 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 | 284 | 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 | 271 | 2019 |
The randomized dependence coefficient D Lopez-Paz, P Hennig, B Schölkopf Advances in Neural Information Processing Systems (NeurIPS) 26, 2013 | 245 | 2013 |
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 | 242 | 2021 |
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 | 203 | 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 | 188 | 2016 |
Descending through a crowded valley-benchmarking deep learning optimizers RM Schmidt, F Schneider, P Hennig International Conference on Machine Learning, 9367-9376, 2021 | 183 | 2021 |
Dissecting adam: The sign, magnitude and variance of stochastic gradients L Balles, P Hennig International Conference on Machine Learning, 404-413, 2018 | 171 | 2018 |
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 | 160 | 2017 |
Probabilistic line searches for stochastic optimization M Mahsereci, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 28, 2015 | 152 | 2015 |
Coupling adaptive batch sizes with learning rates L Balles, J Romero, P Hennig Uncertainty in Artificial Intelligence (UAI) 2017, 2016 | 132 | 2016 |
Quasi-Newton methods: A new direction P Hennig, M Kiefel The Journal of Machine Learning Research 14 (1), 843-865, 2013 | 130 | 2013 |
Probabilistic ODE solvers with Runge-Kutta means M Schober, D Duvenaud, P Hennig Advances in Neural Information Processing Systems (NeurIPS) 27, 2014 | 125 | 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 | 117 | 2014 |
Active learning of linear embeddings for Gaussian processes R Garnett, MA Osborne, P Hennig Uncertainty in Artificial Intelligence (UAI) 2014, 2013 | 115 | 2013 |