| Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence Mit Press, 2022 | 2567 | 2022 |
| Gaussian processes for big data J Hensman, N Fusi, ND Lawrence arXiv preprint arXiv:1309.6835, 2013 | 1533 | 2013 |
| Deep Gaussian processes A Damianou, ND Lawrence Artificial intelligence and statistics, 207-215, 2013 | 1470 | 2013 |
| Probabilistic non-linear principal component analysis with Gaussian process latent variable models. N Lawrence Journal of machine learning research 6 (11), 2005 | 1341 | 2005 |
| Gaussian process latent variable models for visualisation of high dimensional data N Lawrence Advances in neural information processing systems 16, 2003 | 1200 | 2003 |
| Kernels for vector-valued functions: A review MA Alvarez, L Rosasco, ND Lawrence Foundations and Trends® in Machine Learning 4 (3), 195-266, 2012 | 1081 | 2012 |
| Wifi-slam using gaussian process latent variable models. B Ferris, D Fox, ND Lawrence IJCAI 7 (1), 2480-2485, 2007 | 760 | 2007 |
| Variational information distillation for knowledge transfer S Ahn, SX Hu, A Damianou, ND Lawrence, Z Dai Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 759 | 2019 |
| Fast sparse Gaussian process methods: The informative vector machine N Lawrence, M Seeger, R Herbrich Advances in neural information processing systems 15, 2002 | 747 | 2002 |
| Fast forward selection to speed up sparse Gaussian process regression MW Seeger, CKI Williams, ND Lawrence International Workshop on Artificial Intelligence and Statistics, 254-261, 2003 | 667 | 2003 |
| Bayesian Gaussian process latent variable model M Titsias, ND Lawrence Proceedings of the thirteenth international conference on artificial …, 2010 | 630 | 2010 |
| Challenges in deploying machine learning: a survey of case studies A Paleyes, RG Urma, ND Lawrence ACM computing surveys 55 (6), 1-29, 2022 | 481 | 2022 |
| Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling P Perdikaris, M Raissi, A Damianou, ND Lawrence, GE Karniadakis Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017 | 443 | 2017 |
| Learning to learn with the informative vector machine ND Lawrence, JC Platt Proceedings of the twenty-first international conference on Machine learning, 65, 2004 | 436 | 2004 |
| Batch Bayesian optimization via local penalization J González, Z Dai, P Hennig, N Lawrence Artificial intelligence and statistics, 648-657, 2016 | 430 | 2016 |
| Computationally efficient convolved multiple output Gaussian processes MA Alvarez, ND Lawrence The Journal of Machine Learning Research 12, 1459-1500, 2011 | 410 | 2011 |
| Local distance preservation in the GP-LVM through back constraints ND Lawrence, J Quinonero-Candela Proceedings of the 23rd international conference on Machine learning, 513-520, 2006 | 306 | 2006 |
| Advances in Neural Information Processing Systems Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger Inc.: Red Hook, NY, USA 27, 2014 | 299* | 2014 |
| Non-linear matrix factorization with Gaussian processes ND Lawrence, R Urtasun Proceedings of the 26th annual international conference on machine learning …, 2009 | 295 | 2009 |
| Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves TH1/TFH fate bifurcation in malaria T Lönnberg, V Svensson, KR James, D Fernandez-Ruiz, I Sebina, ... Science immunology 2 (9), eaal2192, 2017 | 294 | 2017 |
Andreas DamianouSpotify在 spotify.com 的电子邮件经过验证
