| Variational continual learning CV Nguyen, Y Li, TD Bui, RE Turner arXiv preprint arXiv:1710.10628, 2017 | 846 | 2017 |
| Gaussian process behaviour in wide deep neural networks Matthews, J Hron, M Rowland, RE Turner, Z Ghahramani International Conference on Learning Representations 4, 2018 | 468 | 2018 |
| Q-prop: Sample-efficient policy gradient with an off-policy critic S Gu, T Lillicrap, Z Ghahramani, RE Turner, S Levine arXiv preprint arXiv:1611.02247, 2016 | 415 | 2016 |
| Two problems with variational expectation maximisation for time-series models RE Turner, M Sahani | 407* | 2011 |
| Invariant models for causal transfer learning M Rojas-Carulla, B Schölkopf, R Turner, J Peters Journal of Machine Learning Research 19 (36), 1-34, 2018 | 389 | 2018 |
| Nonlinear ICA using auxiliary variables and generalized contrastive learning A Hyvarinen, H Sasaki, R Turner The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 365 | 2019 |
| Rényi divergence variational inference Y Li, RE Turner Advances in neural information processing systems 29, 2016 | 360 | 2016 |
| Meta-learning probabilistic inference for prediction J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner arXiv preprint arXiv:1805.09921, 2018 | 327 | 2018 |
| The processing and perception of size information in speech sounds DRR Smith, RD Patterson, R Turner, H Kawahara, T Irino The Journal of the Acoustical Society of America 117 (1), 305-318, 2005 | 299 | 2005 |
| Fast and flexible multi-task classification using conditional neural adaptive processes J Requeima, J Gordon, J Bronskill, S Nowozin, RE Turner Advances in neural information processing systems 32, 2019 | 293 | 2019 |
| Practical deep learning with Bayesian principles K Osawa, S Swaroop, MEE Khan, A Jain, R Eschenhagen, RE Turner, ... Advances in neural information processing systems 32, 2019 | 288 | 2019 |
| Black-box alpha divergence minimization J Hernandez-Lobato, Y Li, M Rowland, T Bui, D Hernández-Lobato, ... International conference on machine learning, 1511-1520, 2016 | 283 | 2016 |
| Deep Gaussian processes for regression using approximate expectation propagation T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner International conference on machine learning, 1472-1481, 2016 | 273 | 2016 |
| Deterministic variational inference for robust bayesian neural networks A Wu, S Nowozin, E Meeds, RE Turner, JM Hernandez-Lobato, AL Gaunt arXiv preprint arXiv:1810.03958, 2018 | 228 | 2018 |
| On sparse variational methods and the Kullback-Leibler divergence between stochastic processes RETZG Alexander G. Matthews, James Hensman Proceedings of the 19th International Conference on Artificial Intelligence …, 2016 | 219* | 2016 |
| A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation TD Bui, J Yan, RE Turner Journal of Machine Learning Research 18 (104), 1-72, 2017 | 200 | 2017 |
| Interpolated policy gradient: Merging on-policy and off-policy gradient estimation for deep reinforcement learning SS Gu, T Lillicrap, RE Turner, Z Ghahramani, B Schölkopf, S Levine Advances in neural information processing systems 30, 2017 | 200 | 2017 |
| Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck International Conference on Machine Learning, 1645-1654, 2017 | 192 | 2017 |
| Convolutional conditional neural processes J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner arXiv preprint arXiv:1910.13556, 2019 | 178 | 2019 |
| Bayesian neural network priors revisited V Fortuin, A Garriga-Alonso, SW Ober, F Wenzel, G Rätsch, RE Turner, ... arXiv preprint arXiv:2102.06571, 2021 | 168 | 2021 |
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
