Jasper Snoek - Google Scholar

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Citations	7155	6757
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Co-authors

Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Hugo LarochelleGoogle BrainVerified email at usherbrooke.ca
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
PrabhatLawrence Berkeley National Lab; UC BerkeleyVerified email at lbl.gov
Kevin Leyton-BrownProfessor of Computer Science, University of British Columbia; Canada CIFAR AI Chair, AmiiVerified email at cs.ubc.ca
James BergstraVerified email at uwaterloo.ca
Holger HoosLeiden University, The Netherlands & University of British Columbia, CanadaVerified email at liacs.nl
Michael (Mike) GelbartUniversity of British ColumbiaVerified email at cs.ubc.ca
Jesse HoeyProfessor of Computer Science, University of WaterlooVerified email at cs.uwaterloo.ca
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Shamim NematiUCSDVerified email at ucsd.edu
Mohammad GhassemiMichigan State UniversityVerified email at msu.edu
Ahmad AklPhD Candidate, University of TorontoVerified email at utoronto.ca

Jasper Snoek

Google Brain

Verified email at google.com

Machine Learning


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, 2951-2959, 2012	3833	2012
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	487	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	451	2016
Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in neural information processing systems, 2004-2012, 2013	394	2013
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	219	2013
Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams arXiv preprint arXiv:1403.5607, 2014	194	2014
Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in neural information processing systems, 2449-2457, 2015	181	2015
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, J Dillon, ... Advances in Neural Information Processing Systems, 13991-14002, 2019	153	2019
Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014	149	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	147	2014
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	83	2018
Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling C Riquelme, G Tucker, J Snoek arXiv preprint arXiv:1802.09127, 2018	82	2018
Winner's curse? On pace, progress, and empirical rigor D Sculley, J Snoek, A Wiltschko, A Rahimi	80	2018
Automated detection of unusual events on stairs J Snoek, J Hoey, L Stewart, RS Zemel, A Mihailidis Image and Vision Computing 27 (1-2), 153-166, 2009	75	2009
Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018	62	2018
Towards a single sensor passive solution for automated fall detection M Belshaw, B Taati, J Snoek, A Mihailidis 2011 Annual International Conference of the IEEE Engineering in Medicine and …, 2011	58	2011
Likelihood ratios for out-of-distribution detection J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, M Depristo, J Dillon, ... Advances in Neural Information Processing Systems, 14707-14718, 2019	57	2019
Nonparametric guidance of autoencoder representations using label information J Snoek, RP Adams, H Larochelle The Journal of Machine Learning Research 13 (1), 2567-2588, 2012	53	2012
Raiders of the lost architecture: Kernels for Bayesian optimization in conditional parameter spaces K Swersky, D Duvenaud, J Snoek, F Hutter, MA Osborne arXiv preprint arXiv:1409.4011, 2014	52	2014
Machine learning approaches in cardiovascular imaging M Henglin, G Stein, PV Hushcha, J Snoek, AB Wiltschko, S Cheng Circulation: Cardiovascular Imaging 10 (10), e005614, 2017	45	2017

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