Edwin V. Bonilla

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Chris WilliamsProfessor of Machine Learning, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Grigori FursinMLCommons.org ; cTuning.org ; cKnowledge.orgAdresse e-mail validée de cTuning.org
Maurizio FilipponeAssociate Professor - EURECOMAdresse e-mail validée de eurecom.fr
John CavazosAssistant ProfessorAdresse e-mail validée de udel.edu
Olivier TemamDeepMindAdresse e-mail validée de google.com
Fabio RamosUniversity of Sydney and NVIDIAAdresse e-mail validée de sydney.edu.au
John ThomsonHead of AI Academy, appliedAIAdresse e-mail validée de appliedai.de
Pietro MichiardiEURECOMAdresse e-mail validée de eurecom.fr
Amir DezfouliData61, CSIROAdresse e-mail validée de data61.csiro.au
Christophe DubachAssociate Professor, McGill UniversityAdresse e-mail validée de mcgill.ca
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
Kurt CutajarAmazonAdresse e-mail validée de amazon.com
Timothy M. JonesUniversity of CambridgeAdresse e-mail validée de cl.cam.ac.uk
Ehsan AbbasnejadAustralian Institute for Machine Learning, University of AdelaideAdresse e-mail validée de adelaide.edu.au
Theodoros (Theo) DamoulasProfessor, University of Warwick - UKRI Turing AI FellowAdresse e-mail validée de warwick.ac.uk
Hugh LeatherUniversity of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Pantelis ElinasData61/CSIROAdresse e-mail validée de data61.csiro.au
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyAdresse e-mail validée de tu-berlin.de
Björn FrankeProfessor in the School of Informatics, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk

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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, C Williams, KM Chai Advances in Neural Information Processing Systems (NeurIPS), 153-160, 2007	1249	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	502	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	333	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	304	2011
Improving Topic Coherence with Regularized Topic Models D Newman, EV Bonilla, W Buntine Advances in Neural Information Processing Systems (NeurIPS), 2011	237	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	160	2008
Random feature expansions for deep Gaussian processes K Cutajar, EV Bonilla, P Michiardi, M Filippone International Conference on Machine Learning (ICML), 884-893, 2017	159*	2017
Kernel multi-task learning using task-specific features EV Bonilla, FV Agakov, CKI Williams International Conference on Artificial Intelligence and Statistics (AISTATS …, 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	110	2010
Gaussian process preference elicitation EV Bonilla, S Guo, S Sanner Advances in Neural Information Processing Systems (NeurIPS), 262-270, 2010	107*	2010
Collaborative Multi-output Gaussian Processes. TV Nguyen, EV Bonilla Uncertainty in Artificial Intelligence (UAI), 643-652, 2014	106	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	95	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 (ICML), 145-153, 2014	68	2014
AutoGP: Exploring the capabilities and limitations of Gaussian process models K Krauth, EV Bonilla, K Cutajar, M Filippone Uncertainty in Artificial Intelligence (UAI), 2017	61	2017
Scalable inference for Gaussian process models with black-box likelihoods A Dezfouli, EV Bonilla Advances in Neural Information Processing Systems (NeurIPS) 28, 2015	60	2015
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 (NeurIPS), 2020	47	2020
Automated variational inference for Gaussian process models TV Nguyen, EV Bonilla Advances in Neural Information Processing Systems (NeurIPS) 27, 2014	44	2014

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