Jasper Snoek

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Citations	13052	11605
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i10-index	44	41

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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
PrabhatLawrence Berkeley National Lab; UC BerkeleyVerified email at lbl.gov
Michael (Mike) GelbartUniversity of British ColumbiaVerified email at cs.ubc.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
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Ahmad AklPhD Candidate, University of TorontoVerified email at utoronto.ca
Shamim NematiUCSDVerified email at ucsd.edu
Mohammad Mahdi GhassemiMichigan State UniversityVerified email at msu.edu

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, 2012	6405	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, 2015	798	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	726	2016
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	714	2019
Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in Neural Information Processing Systems, 2013	614	2013
Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams Uncertainty in Artificial Intelligence, 2014	363	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	333	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	313	2019
Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in Neural Information Processing Systems, 2015	273	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	242	2018
Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014	222	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	208	2018
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	207	2014
Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek International Conference on Learning Representations, 2018	147	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	140*	2018
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	139	2020
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	95	2020
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	90	2017
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	82	2009
Raiders of the lost architecture: Kernels for Bayesian optimization in conditional parameter spaces K Swersky, D Duvenaud, J Snoek, F Hutter, MA Osborne NeurIPS workshop on Bayesian Optimization in Theory and Practice, 2013	79	2013

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