Roman Garnett - Google Scholar Citations

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Michael A OsborneAssociate Professor, University of OxfordVerified email at robots.ox.ac.uk
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordVerified email at robots.ox.ac.uk
Jeff SchneiderCarnegie Mellon UniversityVerified email at cs.cmu.edu
Richard P MannUniversity of Leeds, School of MathematicsVerified email at leeds.ac.uk
Kristian KerstingProfessor of Artificial Intelligence and Machine Learning, TU DarmstadtVerified email at cs.tu-darmstadt.de
Gustavo MalkomesSigOptVerified email at sigopt.com
Wenjie HeAssociate Professor, University of Missouri -St. LouisVerified email at umsl.edu
Marion NeumannWashington University in St. LouisVerified email at wustl.edu
Thomas GärtnerTU Wien (Technical University of Vienna)Verified email at tuwien.ac.at
Yifei MaApplied Scientist, Amazon.ComVerified email at amazon.com
Kilian WeinbergerAssociate Professor of Computer Science, Cornell University, ASAPP ResearchVerified email at cornell.edu
Jacob GardnerUber AI LabsVerified email at uber.com
Philipp HennigUniversity of Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
David SumpterProfessor of Applied Mathematics, Uppsala UniversityVerified email at math.uu.se
Benjamin MoseleyCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Shali JiangWashington University in St. LouisVerified email at wustl.edu
Ashley WardUniversity of SydneyVerified email at sydney.edu.au
James Herbert-ReadDepartment of Zoology, University of CambridgeVerified email at cam.ac.uk
Andrea PernaLife Sciences, University of RoehamptonVerified email at perna.fr
Shirley HoFlatiron Institute, Center for Computational AstrophysicsVerified email at flatironinstitute.org

Roman Garnett

Washington University in St. Louis

Verified email at wustl.edu - Homepage

machine learning active learning active search Bayesian optimization Gaussian processes


Title	Cited by	Year
A universal noise removal algorithm with an impulse detector R Garnett, T Huegerich, C Chui, W He IEEE Transactions on image processing 14 (11), 1747-1754, 2005	623	2005
Gaussian processes for global optimization MA Osborne, R Garnett, SJ Roberts 3rd international conference on learning and intelligent optimization (LION3 …, 2009	207	2009
Bayesian optimization for sensor set selection R Garnett, MA Osborne, SJ Roberts Proceedings of the 9th ACM/IEEE international conference on information …, 2010	108	2010
Bayesian optimal active search and surveying R Garnett, Y Krishnamurthy, X Xiong, J Schneider, R Mann International Conference on Machine Learning (ICML 2012), 2012	78	2012
Active learning of model evidence using Bayesian quadrature M Osborne, R Garnett, Z Ghahramani, DK Duvenaud, SJ Roberts, ... Advances in neural information processing systems, 46-54, 2012	71	2012
Propagation kernels: efficient graph kernels from propagated information M Neumann, R Garnett, C Bauckhage, K Kersting Machine Learning 102 (2), 209-245, 2016	63	2016
Sequential Bayesian prediction in the presence of changepoints and faults R Garnett, MA Osborne, S Reece, A Rogers, SJ Roberts The Computer Journal 53 (9), 1430-1446, 2010	59	2010
Multi-scale inference of interaction rules in animal groups using Bayesian model selection RP Mann, A Perna, D Strömbom, R Garnett, JE Herbert-Read, ... PLoS computational biology 9 (3), e1002961, 2013	57	2013
Efficient graph kernels by randomization M Neumann, N Patricia, R Garnett, K Kersting Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012	50	2012
Sampling for inference in probabilistic models with fast Bayesian quadrature T Gunter, MA Osborne, R Garnett, P Hennig, SJ Roberts Advances in neural information processing systems, 2789-2797, 2014	49	2014
Sequential Bayesian prediction in the presence of changepoints R Garnett, MA Osborne, SJ Roberts Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	45	2009
Active search on graphs X Wang, R Garnett, J Schneider Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013	42	2013
Bayesian optimization for automated model selection G Malkomes, C Schaff, R Garnett Advances in Neural Information Processing Systems, 2900-2908, 2016	39	2016
Active learning of linear embeddings for Gaussian processes R Garnett, MA Osborne, P Hennig arXiv preprint arXiv:1310.6740, 2013	39	2013
Objectively identifying landmark use and predicting flight trajectories of the homing pigeon using Gaussian processes R Mann, R Freeman, M Osborne, R Garnett, C Armstrong, J Meade, ... Journal of The Royal Society Interface 8 (55), 210-219, 2010	34	2010
Power iterated color refinement K Kersting, M Mladenov, R Garnett, M Grohe Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014	29	2014
Σ-optimality for active learning on Gaussian random fields Y Ma, R Garnett, J Schneider Advances in Neural Information Processing Systems, 2751-2759, 2013	27	2013
Differentially private Bayesian optimization M Kusner, J Gardner, R Garnett, K Weinberger International Conference on Machine Learning, 918-927, 2015	25	2015
Bayesian quadrature for ratios M Osborne, R Garnett, S Roberts, C Hart, S Aigrain, N Gibson Artificial Intelligence and Statistics, 832-840, 2012	24	2012
Discovering and exploiting additive structure for Bayesian optimization J Gardner, C Guo, K Weinberger, R Garnett, R Grosse Artificial Intelligence and Statistics, 1311-1319, 2017	20	2017

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