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 Columbia + AmiiVerified 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
Howie ChosetProfessor of Robotics, Carnegie MellonVerified email at cs.cmu.edu
Kaustav DasOSD Microsoft Verified email at microsoft.com
Hy TracAssociate Professor, Department of Physics, McWilliams Center for Cosmology, Carnegie MellonVerified email at andrew.cmu.edu
Shirley HoFlatiron Institute, Center for Computational AstrophysicsVerified email at flatironinstitute.org
Matthew A. TeschRobotics Institute, Carnegie Mellon UniversityVerified email at cmu.edu
Xuezhi WangResearch Scientist, Google Brain / DeepMindVerified 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	828	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	590	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	501	2019
Autonomous helicopter control using reinforcement learning policy search methods JA Bagnell, JG Schneider Proceedings 2001 ICRA. IEEE International Conference on Robotics and …, 2001	417	2001
Distributed value functions J Schneider, WK Wong, A Moore, M Riedmiller Carnegie Mellon University, 1999	394	1999
High dimensional Bayesian optimisation and bandits via additive models K Kandasamy, J Schneider, B Póczos International conference on machine learning, 295-304, 2015	339	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	294	2009
Detecting anomalous records in categorical datasets K Das, J Schneider Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007	260	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	252	2004
Policy search by dynamic programming J Bagnell, SM Kakade, J Schneider, A Ng Advances in neural information processing systems 16, 2003	226	2003
Parallelised Bayesian optimisation via Thompson sampling K Kandasamy, A Krishnamurthy, J Schneider, B Póczos International Conference on Artificial Intelligence and Statistics, 133-142, 2018	220	2018
Covariant policy search JA Bagnell, J Schneider Carnegie Mellon University, 2003	218	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	214	2001
Multi-fidelity bayesian optimisation with continuous approximations K Kandasamy, G Dasarathy, J Schneider, B Póczos International Conference on Machine Learning, 1799-1808, 2017	211	2017
Equivariance through parameter-sharing S Ravanbakhsh, J Schneider, B Poczos International conference on machine learning, 2892-2901, 2017	207	2017
Deep learning with sets and point clouds S Ravanbakhsh, J Schneider, B Poczos arXiv preprint arXiv:1611.04500, 2016	195	2016
On the error of random Fourier features DJ Sutherland, J Schneider arXiv preprint arXiv:1506.02785, 2015	192	2015
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	175	2020
Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving N Djuric, V Radosavljevic, H Cui, T Nguyen, FC Chou, TH Lin, N Singh, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020	166	2020
Multi-label output codes using canonical correlation analysis Y Zhang, J Schneider Proceedings of the fourteenth international conference on artificial …, 2011	164	2011

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