Jeff Schneider

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Andrew MooreDean, School of Computer Science, Carnegie MellonVerified email at cs.cmu.edu
Kirthevasan KandasamyUniversity of California, BerkeleyVerified email at cs.cmu.edu
Willie NeiswangerPostdoc, Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Tzu-Kuo HuangResearch Scientist, Google BrainVerified email at google.com
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UVerified email at cs.cmu.edu
Junier OlivaAssistant Professor, University of North CarolinaVerified email at cs.unc.edu
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Roman GarnettWashington University in St. LouisVerified email at wustl.edu
Danica J. SutherlandUniversity of British ColumbiaVerified email at cs.ubc.ca
J. Andrew BagnellCarnegie Mellon UniversityVerified email at ri.cmu.edu
Christopher J. MillerProfessor of Astronomy, University of MichiganVerified email at umich.edu
Kaustav DasOSD Microsoft Verified email at microsoft.com
Howie ChosetProfessor of Robotics, Carnegie MellonVerified email at cs.cmu.edu
Hy TracAssociate Professor, Department of Physics, McWilliams Center for Cosmology, Carnegie MellonVerified email at andrew.cmu.edu
Matthew A. TeschRobotics Institute, Carnegie Mellon UniversityVerified email at cmu.edu
Shirley HoFlatiron Institute, Center for Computational AstrophysicsVerified email at flatironinstitute.org
Xuezhi WangResearch Scientist, Google BrainVerified email at google.com
Yifei MaApplied Scientist, Amazon.ComVerified email at amazon.com
Geoff GordonProfessor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Xi ChenProfessor at New York UniversityVerified email at nyu.edu

Jeff Schneider

Carnegie Mellon University

Verified email at cs.cmu.edu - Homepage

Machine Learning


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Temporal collaborative filtering with bayesian probabilistic tensor factorization L Xiong, X Chen, TK Huang, J Schneider, JG Carbonell Proceedings of the 2010 SIAM international conference on data mining, 211-222, 2010	807	2010
Neural architecture search with bayesian optimisation and optimal transport K Kandasamy, W Neiswanger, J Schneider, B Poczos, EP Xing Advances in neural information processing systems 31, 2018	535	2018
Multimodal trajectory predictions for autonomous driving using deep convolutional networks H Cui, V Radosavljevic, FC Chou, TH Lin, T Nguyen, TK Huang, ... 2019 International Conference on Robotics and Automation (ICRA), 2090-2096, 2019	424	2019
Autonomous helicopter control using reinforcement learning policy search methods JA Bagnell, JG Schneider Proceedings 2001 ICRA. IEEE International Conference on Robotics and …, 2001	412	2001
Distributed value functions J Schneider, WK Wong, A Moore, M Riedmiller Carnegie Mellon University, 1999	327	1999
High dimensional Bayesian optimisation and bandits via additive models K Kandasamy, J Schneider, B Póczos International conference on machine learning, 295-304, 2015	323	2015
Efficiently learning the accuracy of labeling sources for selective sampling P Donmez, JG Carbonell, J Schneider Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	297	2009
Detecting anomalous records in categorical datasets K Das, J Schneider Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007	264	2007
Approximate solutions for partially observable stochastic games with common payoffs R Emery-Montemerlo, G Gordon, J Schneider, S Thrun Proceedings of the Third International Joint Conference on Autonomous Agents …, 2004	251	2004
Covariant policy search JA Bagnell, J Schneider Carnegie Mellon University, 2003	219	2003
Policy search by dynamic programming J Bagnell, SM Kakade, J Schneider, A Ng Advances in neural information processing systems 16, 2003	216	2003
Controlling the false-discovery rate in astrophysical data analysis CJ Miller, C Genovese, RC Nichol, L Wasserman, A Connolly, D Reichart, ... The Astronomical Journal 122 (6), 3492, 2001	212	2001
Parallelised Bayesian optimisation via Thompson sampling K Kandasamy, A Krishnamurthy, J Schneider, B Póczos International Conference on Artificial Intelligence and Statistics, 133-142, 2018	199	2018
Multi-fidelity bayesian optimisation with continuous approximations K Kandasamy, G Dasarathy, J Schneider, B Póczos International Conference on Machine Learning, 1799-1808, 2017	194	2017
Equivariance through parameter-sharing S Ravanbakhsh, J Schneider, B Poczos International conference on machine learning, 2892-2901, 2017	188	2017
Deep learning with sets and point clouds S Ravanbakhsh, J Schneider, B Poczos arXiv preprint arXiv:1611.04500, 2016	185	2016
On the error of random Fourier features DJ Sutherland, J Schneider arXiv preprint arXiv:1506.02785, 2015	185	2015
Multi-label output codes using canonical correlation analysis Y Zhang, J Schneider Proceedings of the fourteenth international conference on artificial …, 2011	163	2011
Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly K Kandasamy, KR Vysyaraju, W Neiswanger, B Paria, CR Collins, ... The Journal of Machine Learning Research 21 (1), 3098-3124, 2020	156	2020
Gaussian process bandit optimisation with multi-fidelity evaluations K Kandasamy, G Dasarathy, JB Oliva, J Schneider, B Póczos Advances in neural information processing systems 29, 2016	151	2016

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