Jasper Snoek - Google Scholar Citations

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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 HoeyAssociate Professor 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


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Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in neural information processing systems, 2951-2959, 2012	3527	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	449	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	414	2016
Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in neural information processing systems, 2004-2012, 2013	367	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	202	2013
Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams arXiv preprint arXiv:1403.5607, 2014	181	2014
Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in neural information processing systems, 2449-2457, 2015	168	2015
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	142	2014
Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014	138	2014
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	88	2019
Winner's curse? On pace, progress, and empirical rigor D Sculley, J Snoek, A Wiltschko, A Rahimi	76	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	74	2009
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	71	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	68	2018
Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018	56	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	56	2011
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	52	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	50	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	42	2017
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	38	2019

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