Jasper Snoek

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Citations	15130	13674
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Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Hugo LarochelleGoogle Brain & MilaVerified email at google.com
Kevin SwerskyGoogle BrainVerified email at cs.toronto.edu
Alex MihailidisProfessor, Department of Occupational Science & Occupational Therapy, University of TorontoVerified email at utoronto.ca
Babak TaatiScientist & Assistant Professor, KITE | Toronto Rehab - UHN & University of TorontoVerified email at uhn.ca
Oren RippelMIT, Harvard UniversityVerified email at mit.edu
David R KelleyCalico LabsVerified email at calicolabs.com
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Michael (Mike) GelbartVancouver Independent School for Science and TechnologyVerified email at visst.ca
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
James BergstraVerified email at uwaterloo.ca
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Jesse HoeyProfessor of Computer Science, University of WaterlooVerified email at cs.uwaterloo.ca
Ahmad AklPhD Candidate, University of TorontoVerified email at utoronto.ca
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Shamim NematiUCSDVerified email at ucsd.edu
Mohammad Mahdi GhassemiMichigan State UniversityVerified email at msu.edu
Hrishikesh AradhyeGoogle ResearchVerified email at google.com

Jasper Snoek

Google Brain

Verified email at google.com

Machine Learning


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Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in Neural Information Processing Systems, 2012	7205	2012
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ... Advances in Neural Information Processing Systems, 2019	938	2019
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, 2015	926	2015
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks DR Kelley, J Snoek, JL Rinn Genome research 26 (7), 990-999, 2016	795	2016
Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in Neural Information Processing Systems, 2013	680	2013
Likelihood ratios for out-of-distribution detection J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, MA DePristo, JV Dillon, ... Advances in Neural Information Processing Systems, 2019	424	2019
Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams Uncertainty in Artificial Intelligence, 2014	424	2014
Towards an empirical foundation for assessing bayesian optimization of hyperparameters K Eggensperger, M Feurer, F Hutter, J Bergstra, J Snoek, H Hoos, ... NIPS workshop on Bayesian Optimization in Theory and Practice 10 (3), 2013	363	2013
Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in Neural Information Processing Systems, 2015	307	2015
Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling C Riquelme, G Tucker, J Snoek International Conference on Learning Representations, 2018	288	2018
Sequential regulatory activity prediction across chromosomes with convolutional neural networks DR Kelley, YA Reshef, M Bileschi, D Belanger, CY McLean, J Snoek Genome research 28 (5), 739-750, 2018	257	2018
Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014	239	2014
Input warping for Bayesian optimization of non-stationary functions J Snoek, K Swersky, R Zemel, R Adams International Conference on Machine Learning, 1674-1682, 2014	222	2014
How good is the bayes posterior in deep neural networks really? F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ... International Conference on Machine Learning, 2020	202	2020
Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek International Conference on Learning Representations, 2018	164	2018
Winner's curse? On pace, progress, and empirical rigor D Sculley, J Snoek, A Wiltschko, A Rahimi International Conference on Learning Representations Workshops, 2018	156*	2018
Efficient and scalable bayesian neural nets with rank-1 factors M Dusenberry, G Jerfel, Y Wen, Y Ma, J Snoek, K Heller, ... International conference on machine learning, 2782-2792, 2020	129	2020
Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton Advances in Neural Information Processing Systems, 2020	108	2020
Training independent subnetworks for robust prediction M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ... International Conference on Learning Representations, 2021	99	2021
Second opinion needed: communicating uncertainty in medical machine learning B Kompa, J Snoek, AL Beam NPJ Digital Medicine 4 (1), 1-6, 2021	97	2021

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