Edwin V. Bonilla

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Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Grigori FursinOctoML.ai ; cTuning.org ; MLCommons.orgVerified email at cTuning.org
Maurizio FilipponeAssociate Professor - EURECOMVerified email at eurecom.fr
John ThomsonLecturer, University of St AndrewsVerified email at st-andrews.ac.uk
John CavazosAssistant ProfessorVerified email at udel.edu
Olivier TemamINRIAVerified email at inria.fr
Christophe DubachAssociate Professor, McGill UniversityVerified email at mcgill.ca
Scott SannerUniversity of TorontoVerified email at mie.utoronto.ca
Trung V. NguyenMachine Learning, ANU and NICTAVerified email at nicta.com.au
Pietro MichiardiEURECOMVerified email at eurecom.fr
Fabio RamosUniversity of Sydney and NVIDIAVerified email at sydney.edu.au
Amir DezfouliData61, CSIROVerified email at data61.csiro.au
Timothy M. JonesUniversity of CambridgeVerified email at cl.cam.ac.uk
Kurt CutajarAmazonVerified email at amazon.com
Elad Yom-TovMicrosoft ResearchVerified email at yom-tov.info
Ehsan AbbasnejadAustralian Institute for Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Hugh LeatherUniversity of EdinburghVerified email at inf.ed.ac.uk
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de
Björn FrankeProfessor in the School of Informatics, University of EdinburghVerified email at inf.ed.ac.uk
Theodoros (Theo) DamoulasProfessor, University of Warwick - UKRI Turing AI FellowVerified email at warwick.ac.uk

Edwin V. Bonilla

Principal Research Scientist, CSIRO's Data61

Verified email at data61.csiro.au - Homepage

Machine Learning Bayesian Statistics Probabilistic Inference Artificial Intelligence Gaussian Processes


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Multi-task Gaussian process prediction EV Bonilla, C Williams, KM Chai Advances in neural information processing systems, 153-160, 2007	1026*	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	478	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	298	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 (3), 296-327, 2011	259	2011
Improving Topic Coherence with Regularized Topic Models D Newman, EV Bonilla, W Buntine Advances in Neural Information Processing Systems, 2011	203	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	190	2014
MILEPOST GCC: machine learning based research compiler G Fursin, C Miranda, O Temam, M Namolaru, E Yom-Tov, A Zaks, ... GCC Summit, 2008	145	2008
Kernel multi-task learning using task-specific features EV Bonilla, FV Agakov, CKI Williams Artificial Intelligence and Statistics, 43-50, 2007	132	2007
Random feature expansions for deep Gaussian processes K Cutajar, EV Bonilla, P Michiardi, M Filippone International Conference on Machine Learning, 884-893, 2017	112*	2017
Gaussian process preference elicitation EV Bonilla, S Guo, S Sanner Advances in neural information processing systems, 262-270, 2010	100*	2010
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	99	2010
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	93	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	92	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	79	2009
Collaborative Multi-output Gaussian Processes. TV Nguyen, EV Bonilla UAI, 643-652, 2014	77	2014
Scalable inference for Gaussian process models with black-box likelihoods A Dezfouli, EV Bonilla Advances in Neural Information Processing Systems 28, 1414-1422, 2015	61	2015
Fast allocation of Gaussian process experts T Nguyen, E Bonilla International Conference on Machine Learning, 145-153, 2014	58	2014
AutoGP: Exploring the capabilities and limitations of Gaussian process models K Krauth, EV Bonilla, K Cutajar, M Filippone Uncertainty in Artificial Intelligence, 2017	46	2017
Automated variational inference for Gaussian process models TV Nguyen, EV Bonilla Advances in Neural Information Processing Systems 27, 1404-1412, 2014	40	2014
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	39	2013

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