Ryan P. Adams

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David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Hugo LarochelleGoogle DeepMind & MilaVerified email at google.com
Matthew J. JohnsonGoogle BrainVerified email at csail.mit.edu
Iain MurraySchool of Informatics, University of Edinburgh and AmazonVerified email at ed.ac.uk
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeVerified email at mrao.cam.ac.uk
Nando de FreitasCIFAR & DeepMindVerified email at google.com
George E. DahlGoogle Inc.Verified email at google.com
Abigail G. DoyleUCLAVerified email at chem.ucla.edu
Jamie KirosOpenAIVerified email at openai.com
Elaine AngelinoMiller Fellow, UC BerkeleyVerified email at eecs.berkeley.edu
Peter OrbanzColumbia UniversityVerified email at stat.columbia.edu
Emily B. FoxStanford UniversityVerified email at stanford.edu
Margo SeltzerUniversity of British ColumbiaVerified email at cs.ubc.ca
Diana CaiCenter for Computational Mathematics, Flatiron InstituteVerified email at flatironinstitute.org
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Hanna WallachPartner Research Manager, Microsoft ResearchVerified email at dirichlet.net
Sigrid AdriaenssensPrinceton UniversityVerified email at princeton.edu
Joshua B. TenenbaumMITVerified email at mit.edu

Ryan P. Adams

Princeton University

Verified email at princeton.edu - Homepage

Machine Learning Artificial Intelligence Statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in neural information processing systems 25, 2012	9435	2012
Taking the human out of the loop: A review of Bayesian optimization B Shahriari, K Swersky, Z Wang, RP Adams, N De Freitas Proceedings of the IEEE 104 (1), 148-175, 2015	4996	2015
Convolutional networks on graphs for learning molecular fingerprints DK Duvenaud, D Maclaurin, J Iparraguirre, R Bombarell, T Hirzel, ... Advances in neural information processing systems 28, 2015	4084	2015
Automatic chemical design using a data-driven continuous representation of molecules R Gómez-Bombarelli, JN Wei, D Duvenaud, JM Hernández-Lobato, ... ACS central science 4 (2), 268-276, 2018	3070	2018
Scalable bayesian optimization using deep neural networks J Snoek, O Rippel, K Swersky, R Kiros, N Satish, N Sundaram, M Patwary, ... International conference on machine learning, 2171-2180, 2015	1228	2015
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International conference on machine learning, 1861-1869, 2015	1076	2015
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	1006	2015
Bayesian online changepoint detection RP Adams, DJC MacKay arXiv preprint arXiv:0710.3742, 2007	1006	2007
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach R Gómez-Bombarelli, J Aguilera-Iparraguirre, TD Hirzel, D Duvenaud, ... Nature materials 15 (10), 1120-1127, 2016	905	2016
Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in neural information processing systems 26, 2013	860	2013
Gaussian process kernels for pattern discovery and extrapolation A Wilson, R Adams International conference on machine learning, 1067-1075, 2013	770	2013
Mapping sub-second structure in mouse behavior AB Wiltschko, MJ Johnson, G Iurilli, RE Peterson, JM Katon, ... Neuron 88 (6), 1121-1135, 2015	666	2015
Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams arXiv preprint arXiv:1403.5607, 2014	597	2014
Composing graphical models with neural networks for structured representations and fast inference MJ Johnson, DK Duvenaud, A Wiltschko, RP Adams, SR Datta Advances in neural information processing systems 29, 2016	532	2016
Elliptical slice sampling I Murray, R Adams, D MacKay Proceedings of the thirteenth international conference on artificial …, 2010	528	2010
Bayesian reaction optimization as a tool for chemical synthesis BJ Shields, J Stevens, J Li, M Parasram, F Damani, JIM Alvarado, ... Nature 590 (7844), 89-96, 2021	506	2021
Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in neural information processing systems 28, 2015	388	2015
Advances in neural information processing systems J Snoek, H Larochelle, RP Adams, F Pereira, CJC Burges, L Bottou, ... Practical Bayesian optimization of machine learning algorithms, 2951-9, 2012	385	2012
Discovering latent network structure in point process data S Linderman, R Adams International conference on machine learning, 1413-1421, 2014	337	2014
Autograd: Effortless gradients in numpy D Maclaurin, D Duvenaud, RP Adams ICML 2015 AutoML workshop 238 (5), 2015	301	2015

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