| Batch Bayesian optimization via local penalization J González, Z Dai, P Hennig, N Lawrence Artificial intelligence and statistics, 648-657, 2016 | 198 | 2016 |
| Variational auto-encoded deep Gaussian processes Z Dai, A Damianou, J González, N Lawrence ICLR 2016, 2015 | 111 | 2015 |
| Protein biogenesis machinery is a driver of replicative aging in yeast GE Janssens, AC Meinema, J Gonzalez, JC Wolters, A Schmidt, V Guryev, ... elife 4, e08527, 2015 | 99 | 2015 |
| GLASSES: Relieving the myopia of Bayesian optimisation J González, M Osborne, N Lawrence Artificial Intelligence and Statistics, 790-799, 2016 | 76 | 2016 |
| Bayesian optimization for synthetic gene design J González, J Longwoth, DC James, ND Lawrence NIPS Workshop in Bayesian Optimization, 2014 | 52 | 2014 |
| Construction and use of a microfluidic dissection platform for long-term imaging of cellular processes in budding yeast DHEW Huberts, SS Lee, J González, GE Janssens, IA Vizcarra, ... Nature protocols 8 (6), 1019-1027, 2013 | 42 | 2013 |
| Representing functional data using support vector machines A Muñoz, J González Pattern Recognition Letters 31 (6), 511-516, 2010 | 42* | 2010 |
| Preferential Bayesian Optimization J Gonzalez, Z Dai, A Damianou, ND Lawrence International Conference on Machine Learning, PMLR 70:1282-1291, 2017, 2017 | 38 | 2017 |
| Bayesian optimization with tree-structured dependencies R Jenatton, C Archambeau, J González, M Seeger International Conference on Machine Learning, ICML 2017., 1655-1664, 2017 | 37 | 2017 |
| GPy: A gaussian process framework in python GP GPy | 36* | 2012 |
| A robust partial least squares regression method with applications J González, D Peña, R Romera Journal of Chemometrics 23 (2), 78-90, 2009 | 35 | 2009 |
| Calorie restriction does not elicit a robust extension of replicative lifespan in Saccharomyces cerevisiae DHEW Huberts, J González, SS Lee, A Litsios, G Hubmann, EC Wit, ... Proceedings of the National Academy of Sciences 111 (32), 11727-11731, 2014 | 34 | 2014 |
| GPyOpt: A Bayesian optimization framework in Python ZD Javier Gonzalez https://github.com/SheffieldML/GPyOpt, 2016 | 29* | 2016 |
| Deep gaussian processes for multi-fidelity modeling K Cutajar, M Pullin, A Damianou, N Lawrence, J González arXiv preprint arXiv:1903.07320, 2019 | 27 | 2019 |
| Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations J González, I Vujačić, E Wit Pattern Recognition Letters 45, 26-32, 2014 | 26 | 2014 |
| Inferring latent gene regulatory network kinetics J González, I Vujačić, E Wit Statistical applications in genetics and molecular biology 12 (1), 109-127, 2013 | 25 | 2013 |
| Structured variationally auto-encoded optimization X Lu, J Gonzalez, Z Dai, N Lawrence International Conference on Machine Learning, ICML 2020., 3267-3275, 2018 | 21 | 2018 |
| Time-course window estimator for ordinary differential equations linear in the parameters I Vujačić, I Dattner, J González, E Wit Statistics and Computing, 1-14, 2014 | 20 | 2014 |
| Emulation of physical processes with Emukit A Paleyes, M Pullin, M Mahsereci, N Lawrence, J Gonzalez NeurIPS Workshop on Machine Learning and the Physical Sciences, 2019 | 18 | 2019 |
| On the generalization of the mahalanobis distance G Martos, A Muñoz, J González Iberoamerican Congress on Pattern Recognition, 125-132, 2013 | 18 | 2013 |
Neil D. LawrenceUniversity of CambridgeAdresse e-mail validée de cl.cam.ac.uk
