| Variational Information Distillation for Knowledge Transfer S Ahn, SX Hu, A Damianou, ND Lawrence, Z Dai IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019 | 978 | 2019 |
| Batch Bayesian Optimization via Local Penalization J González, Z Dai, P Hennig, ND Lawrence International Conference on Artificial Intelligence and Statistics, 2015 | 530 | 2015 |
| Variational Auto-encoded Deep Gaussian Processes Z Dai, A Damianou, J González, N Lawrence International Conference on Learning Representations (ICLR), 2015 | 198 | 2015 |
| GPy: A Gaussian process framework in python GPy https://github.com/SheffieldML/GPy, 2012 | 180* | 2012 |
| Preferential Bayesian Optimization J Gonzalez, Z Dai, A Damianou, ND Lawrence International Conference on Machine Learning, 2017 | 174 | 2017 |
| Recurrent Gaussian Processes CLC Mattos, Z Dai, A Damianou, J Forth, GA Barreto, ND Lawrence International Conference on Learning Representations (ICLR), 2015 | 101* | 2015 |
| Structured variationally auto-encoded optimization X Lu, J Gonzalez, Z Dai, ND Lawrence International conference on machine learning, 3267-3275, 2018 | 87 | 2018 |
| Data-driven mode identification and unsupervised fault detection for nonlinear multimode processes B Wang, Z Li, Z Dai, N Lawrence, X Yan IEEE Transactions on Industrial Informatics 16 (6), 3651-3661, 2019 | 79 | 2019 |
| Auto-differentiating linear algebra M Seeger, A Hetzel, Z Dai, E Meissner, ND Lawrence arXiv preprint arXiv:1710.08717, 2017 | 57 | 2017 |
| Meta-surrogate benchmarking for hyperparameter optimization A Klein, Z Dai, F Hutter, N Lawrence, J Gonzalez Advances in Neural Information Processing Systems 32, 2019 | 54 | 2019 |
| Intrinsic Gaussian processes on complex constrained domains M Niu, P Cheung, L Lin, Z Dai, N Lawrence, D Dunson Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2019 | 50 | 2019 |
| A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant B Wang, Z Li, Z Dai, N Lawrence, X Yan Applied Soft Computing 82, 105527, 2019 | 40 | 2019 |
| GPyOpt: a Bayesian optimization framework in Python J González, Z Dai J. Mach. Learn. Res 7, 1-5, 2016 | 39 | 2016 |
| Gaussian process models with parallelization and GPU acceleration Z Dai, A Damianou, J Hensman, N Lawrence arXiv preprint arXiv:1410.4984, 2014 | 37 | 2014 |
| Deep recurrent Gaussian processes for outlier-robust system identification CLC Mattos, Z Dai, A Damianou, GA Barreto, ND Lawrence Journal of Process Control 60, 82-94, 2017 | 32 | 2017 |
| Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes Z Dai, MA Álvarez, ND Lawrence Advances in Neural Information Processing Systems, 2017 | 31 | 2017 |
| Autonomous Document Cleaning—A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts Z Dai, J Lucke IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (10), 1950 …, 2014 | 28 | 2014 |
| GP-select: Accelerating EM using adaptive subspace preselection JA Shelton, J Gasthaus, Z Dai, J Lücke, A Gretton Neural Computation 29 (8), 2177-2202, 2017 | 24 | 2017 |
| GPyOpt: A Bayesian optimization framework in python T GPyOpt authors | 24 | 2016 |
| Automatic music playlist generation via simulation-based reinforcement learning F Tomasi, J Cauteruccio, S Kanoria, K Ciosek, M Rinaldi, Z Dai Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 23 | 2023 |
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
