| Variational continual learning CV Nguyen, Y Li, TD Bui, RE Turner International Conference on Learning Representations (ICLR), 2018 | 832 | 2018 |
| Black-box α-divergence minimization JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ... Proceedings of The 33rd International Conference on Machine Learning (ICML), 2016 | 278 | 2016 |
| Deep Gaussian processes for regression using approximate expectation propagation TD Bui, D Hernández-Lobato, Y Li, JM Hernández-Lobato, RE Turner Proceedings of The 33rd International Conference on Machine Learning (ICML), 2016 | 272 | 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 | 196 | 2017 |
| Neural graph learning: Training neural networks using graphs TD Bui, S Ravi, V Ramavajjala Proceedings of the Eleventh ACM International Conference on Web Search and …, 2018 | 132* | 2018 |
| Streaming sparse Gaussian process approximations TD Bui, CV Nguyen, RE Turner Advances in Neural Information Processing Systems 30 (NeurIPS), 2017 | 132 | 2017 |
| Learning stationary time series using Gaussian processes with nonparametric kernels F Tobar, T Bui, R Turner Advances in Neural Information Processing Systems 28 (NeurIPS), 2015 | 105 | 2015 |
| Partitioned variational inference: A unified framework encompassing federated and continual learning TD Bui, CV Nguyen, S Swaroop, RE Turner arXiv preprint arXiv:1811.11206, 2018 | 66 | 2018 |
| Tree-structured Gaussian Process Approximations TD Bui, RE Turner Advances in Neural Information Processing Systems, 2213-2221, 2014 | 61 | 2014 |
| Improving and understanding variational continual learning S Swaroop, CV Nguyen, TD Bui, RE Turner arXiv preprint arXiv:1905.02099, 2019 | 60 | 2019 |
| Variational auto-regressive Gaussian processes for continual learning S Kapoor, T Karaletsos, TD Bui International Conference on Machine Learning, 5290-5300, 2021 | 31 | 2021 |
| Hierarchical Gaussian process priors for Bayesian neural network weights T Karaletsos, TD Bui Advances in Neural Information Processing Systems 33 (NeurIPS), 2020 | 31 | 2020 |
| Stochastic variational inference for Gaussian process latent variable models using back constraints TD Bui, RE Turner Black Box Learning and Inference NIPS workshop, 2015 | 20 | 2015 |
| Natural Variational Continual Learning H Tseran, ME Khan, T Harada, TD Bui NeurIPS Continual Learning Workshop, 2018 | 19 | 2018 |
| q-Paths: Generalizing the Geometric Annealing Path using Power Means V Masrani, R Brekelmans, T Bui, F Nielsen, A Galstyan, GV Steeg, ... 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021 | 18 | 2021 |
| Training deep Gaussian processes using stochastic expectation propagation and probabilistic backpropagation TD Bui, JM Hernández-Lobato, Y Li, D Hernández-Lobato, RE Turner arXiv preprint arXiv:1511.03405, 2015 | 18 | 2015 |
| Design of covariance functions using inter-domain inducing variables F Tobar, TD Bui, RE Turner NIPS Time Series Workshop, 2015 | 14 | 2015 |
| Annealed importance sampling with q-paths R Brekelmans, V Masrani, T Bui, F Wood, A Galstyan, GV Steeg, ... arXiv preprint arXiv:2012.07823, 2020 | 12 | 2020 |
| Astrollama: Towards specialized foundation models in astronomy TD Nguyen, YS Ting, I Ciucă, C O'Neill, ZC Sun, M Jabłońska, S Kruk, ... arXiv preprint arXiv:2309.06126, 2023 | 11 | 2023 |
| Partitioned variational inference: A framework for probabilistic federated learning M Ashman, TD Bui, CV Nguyen, S Markou, A Weller, S Swaroop, ... arXiv preprint arXiv:2202.12275, 2022 | 11 | 2022 |
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
