Edwin V. Bonilla

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Coauteurs

Chris WilliamsProfessor of Machine Learning, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Grigori FursincKnowledge.io ; cTuning.org ; MLCommons.orgAdresse e-mail validée de cTuning.org
John CavazosAssistant ProfessorAdresse e-mail validée de udel.edu
John ThomsonLecturer, University of St AndrewsAdresse e-mail validée de st-andrews.ac.uk
Olivier TemamINRIAAdresse e-mail validée de inria.fr
Christophe DubachAssociate Professor, McGill UniversityAdresse e-mail validée de mcgill.ca
Maurizio FilipponeAssociate Professor - EURECOMAdresse e-mail validée de eurecom.fr
Trung V. NguyenMachine Learning, ANU and NICTAAdresse e-mail validée de nicta.com.au
Scott SannerUniversity of TorontoAdresse e-mail validée de mie.utoronto.ca
Hugh LeatherUniversity of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Amir DezfouliData61, CSIROAdresse e-mail validée de data61.csiro.au
Timothy M. JonesUniversity of CambridgeAdresse e-mail validée de cl.cam.ac.uk
Pietro MichiardiEURECOMAdresse e-mail validée de eurecom.fr
Ehsan AbbasnejadAustralian Institute for Machine Learning, University of AdelaideAdresse e-mail validée de adelaide.edu.au
Kurt CutajarAmazonAdresse e-mail validée de amazon.com
Elad Yom-TovMicrosoft ResearchAdresse e-mail validée de yom-tov.info
Fabio RamosUniversity of Sydney and NVIDIAAdresse e-mail validée de sydney.edu.au
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyAdresse e-mail validée de informatik.uni-stuttgart.de
Björn FrankeProfessor in the School of Informatics, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Karl KrauthPhD student, UC BerkeleyAdresse e-mail validée de berkeley.edu

Edwin V. Bonilla

Principal Research Scientist, CSIRO's Data61

Adresse e-mail validée de data61.csiro.au - Page d'accueil

Machine Learning Bayesian Statistics Probabilistic Inference Artificial Intelligence Gaussian Processes


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Multi-task Gaussian process prediction EV Bonilla, KM Chai, C Williams Advances in neural information processing systems, 153-160, 2008	901	2008
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	451	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	273	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	219	2011
Improving Topic Coherence with Regularized Topic Models D Newman, EV Bonilla, W Buntine Advances in Neural Information Processing Systems, 2011	187	2011
Automatic feature generation for machine learning based optimizing compilation H Leather, E Bonilla, M O'Boyle 2009 International Symposium on Code Generation and Optimization, 81-91, 2009	151	2009
MILEPOST GCC: machine learning based research compiler G Fursin, C Miranda, O Temam, M Namolaru, E Yom-Tov, A Zaks, ... GCC Summit, 2008	136	2008
Kernel multi-task learning using task-specific features EV Bonilla, FV Agakov, CKI Williams Artificial Intelligence and Statistics, 43-50, 2007	131	2007
Gaussian process preference elicitation EV Bonilla, S Guo, S Sanner Advances in neural information processing systems, 262-270, 2010	93*	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	91	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	90	2012
Random feature expansions for deep Gaussian processes K Cutajar, EV Bonilla, P Michiardi, M Filippone International Conference on Machine Learning, 884-893, 2017	87*	2017
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	87	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	69	2009
Collaborative Multi-output Gaussian Processes. TV Nguyen, EV Bonilla UAI, 643-652, 2014	63	2014
Fast allocation of Gaussian process experts T Nguyen, E Bonilla International Conference on Machine Learning, 145-153, 2014	51	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	50	2015
AutoGP: Exploring the capabilities and limitations of Gaussian process models K Krauth, EV Bonilla, K Cutajar, M Filippone Uncertainty in Artificial Intelligence, 2017	41	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	37	2013
Automated variational inference for Gaussian process models TV Nguyen, EV Bonilla Advances in Neural Information Processing Systems 27, 1404-1412, 2014	33	2014

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