| Multi-task Gaussian process prediction EV Bonilla, C Williams, KM Chai Advances in neural information processing systems, 153-160, 2007 | 1219 | 2007 |
| Using machine learning to focus iterative optimization F Agakov, E Bonilla, J Cavazos, B Franke, G Fursin, MFP O'Boyle, ... International Symposium on Code Generation and Optimization, 11 pp.-305, 2006 | 497 | 2006 |
| Rapidly selecting good compiler optimizations using performance counters J Cavazos, G Fursin, F Agakov, E Bonilla, MFP O'Boyle, O Temam International Symposium on Code Generation and Optimization, 185-197, 2007 | 329 | 2007 |
| Milepost gcc: Machine learning enabled self-tuning compiler G Fursin, Y Kashnikov, AW Memon, Z Chamski, O Temam, M Namolaru, ... International journal of parallel programming 39, 296-327, 2011 | 299 | 2011 |
| Improving Topic Coherence with Regularized Topic Models D Newman, EV Bonilla, W Buntine Advances in Neural Information Processing Systems, 2011 | 232 | 2011 |
| Automatic feature generation for machine learning--based optimising compilation H Leather, E Bonilla, M O'boyle ACM Transactions on Architecture and Code Optimization 11 (1), 1-32, 2014 | 207 | 2014 |
| MILEPOST GCC: machine learning based research compiler G Fursin, C Miranda, O Temam, M Namolaru, E Yom-Tov, A Zaks, ... GCC Summit, 2008 | 158 | 2008 |
| Random feature expansions for deep Gaussian processes K Cutajar, EV Bonilla, P Michiardi, M Filippone International Conference on Machine Learning, 884-893, 2017 | 156* | 2017 |
| Kernel multi-task learning using task-specific features EV Bonilla, FV Agakov, CKI Williams Artificial Intelligence and Statistics, 43-50, 2007 | 136 | 2007 |
| A predictive model for dynamic microarchitectural adaptivity control C Dubach, TM Jones, EV Bonilla, MFP O'Boyle 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture, 485-496, 2010 | 107 | 2010 |
| Gaussian process preference elicitation EV Bonilla, S Guo, S Sanner Advances in neural information processing systems, 262-270, 2010 | 106* | 2010 |
| Collaborative Multi-output Gaussian Processes. TV Nguyen, EV Bonilla UAI, 643-652, 2014 | 102 | 2014 |
| New objective functions for social collaborative filtering J Noel, S Sanner, KN Tran, P Christen, L Xie, EV Bonilla, E Abbasnejad, ... Proceedings of the 21st international conference on World Wide Web, 859-868, 2012 | 95 | 2012 |
| Automatic performance model construction for the fast software exploration of new hardware designs J Cavazos, C Dubach, F Agakov, E Bonilla, MFP O'Boyle, G Fursin, ... International conference on Compilers, architecture and synthesis for …, 2006 | 94 | 2006 |
| Portable compiler optimisation across embedded programs and microarchitectures using machine learning C Dubach, TM Jones, EV Bonilla, G Fursin, MFP O'Boyle Proceedings of the 42nd Annual IEEE/ACM International Symposium on …, 2009 | 78 | 2009 |
| Fast allocation of Gaussian process experts T Nguyen, E Bonilla International Conference on Machine Learning, 145-153, 2014 | 64 | 2014 |
| Scalable inference for Gaussian process models with black-box likelihoods A Dezfouli, EV Bonilla Advances in Neural Information Processing Systems 28, 2015 | 60 | 2015 |
| AutoGP: Exploring the capabilities and limitations of Gaussian process models K Krauth, EV Bonilla, K Cutajar, M Filippone Uncertainty in Artificial Intelligence, 2017 | 57 | 2017 |
| Learning community-based preferences via dirichlet process mixtures of gaussian processes E Abbasnejad, S Sanner, EV Bonilla, P Poupart International joint conference on artificial intelligence, 2013 | 43 | 2013 |
| Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings P Elinas, EV Bonilla, L Tiao Advances in Neural Information Processing Systems, 2020 | 42 | 2020 |
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
