| Batch Bayesian Optimization via Local Penalization J González, Z Dai, P Hennig, ND Lawrence International Conference on Artificial Intelligence and Statistics, 2015 | 198 | 2015 |
| Variational Auto-encoded Deep Gaussian Processes Z Dai, A Damianou, J González, N Lawrence International Conference on Learning Representations (ICLR), 2015 | 111 | 2015 |
| 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 | 88 | 2019 |
| Recurrent Gaussian Processes CLC Mattos, Z Dai, A Damianou, J Forth, GA Barreto, ND Lawrence International Conference on Learning Representations (ICLR), 2015 | 57 | 2015 |
| GPy: A Gaussian process framework in python GPy https://github.com/SheffieldML/GPy, 2012 | 36* | 2012 |
| Preferential Bayesian Optimization J Gonzalez, Z Dai, A Damianou, ND Lawrence International Conference on Machine Learning, 2017 | 35 | 2017 |
| Gaussian process models with parallelization and GPU acceleration Z Dai, A Damianou, J Hensman, N Lawrence arXiv preprint arXiv:1410.4984, 2014 | 28 | 2014 |
| Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes Z Dai, MA Álvarez, ND Lawrence Advances in Neural Information Processing Systems, 2017 | 26 | 2017 |
| Auto-differentiating linear algebra M Seeger, A Hetzel, Z Dai, E Meissner, ND Lawrence arXiv preprint arXiv:1710.08717, 2017 | 23 | 2017 |
| Structured variationally auto-encoded optimization X Lu, J Gonzalez, Z Dai, ND Lawrence International conference on machine learning, 3267-3275, 2018 | 21 | 2018 |
| GPyOpt: a Bayesian optimization framework in Python J González, Z Dai Accessed, 2016 | 21 | 2016 |
| 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 | 20 | 2014 |
| Polygonal light source estimation D Schnieders, KYK Wong, Z Dai Asian conference on computer vision, 96-107, 2009 | 20 | 2009 |
| Meta-surrogate benchmarking for hyperparameter optimization A Klein, Z Dai, F Hutter, N Lawrence, J Gonzalez arXiv preprint arXiv:1905.12982, 2019 | 17 | 2019 |
| 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 | 16 | 2017 |
| What are the invariant occlusive components of image patches? A probabilistic generative approach Z Dai, G Exarchakis, J Lücke Advances in neural information processing systems, 243-251, 2013 | 16 | 2013 |
| Pose estimation from reflections for specular surface recovery M Liu, KYK Wong, Z Dai, Z Chen 2011 International Conference on Computer Vision, 579-586, 2011 | 16 | 2011 |
| 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 | 15 | 2017 |
| Unsupervised learning of translation invariant occlusive components Z Dai, J Lücke 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2400-2407, 2012 | 15 | 2012 |
| Specular surface recovery from reflections of a planar pattern undergoing an unknown pure translation M Liu, KYK Wong, Z Dai, Z Chen Asian Conference on Computer Vision, 137-147, 2010 | 13 | 2010 |
Neil D. LawrenceUniversity of CambridgeAdresse e-mail validée de cl.cam.ac.uk
