Jeff Schneider

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Andrew MooreDean, School of Computer Science, Carnegie MellonVerified email at cs.cmu.edu
Willie NeiswangerPostdoc, Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Kirthevasan KandasamyUniversity of Wisconsin-MadisonVerified email at cs.wisc.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 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	850	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	651	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	602	2019
Distributed value functions J Schneider, WK Wong, A Moore, M Riedmiller Carnegie Mellon University, 1999	437	1999
Autonomous helicopter control using reinforcement learning policy search methods JA Bagnell, JG Schneider Proceedings 2001 ICRA. IEEE International Conference on Robotics and …, 2001	427	2001
High dimensional Bayesian optimisation and bandits via additive models K Kandasamy, J Schneider, B Póczos International conference on machine learning, 295-304, 2015	380	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	298	2009
Detecting anomalous records in categorical datasets K Das, J Schneider Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007	267	2007
Parallelised Bayesian optimisation via Thompson sampling K Kandasamy, A Krishnamurthy, J Schneider, B Póczos International conference on artificial intelligence and statistics, 133-142, 2018	254	2018
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	253	2004
Multi-fidelity bayesian optimisation with continuous approximations K Kandasamy, G Dasarathy, J Schneider, B Póczos International conference on machine learning, 1799-1808, 2017	236	2017
Equivariance through parameter-sharing S Ravanbakhsh, J Schneider, B Poczos International conference on machine learning, 2892-2901, 2017	232	2017
Policy search by dynamic programming J Bagnell, SM Kakade, J Schneider, A Ng Advances in neural information processing systems 16, 2003	232	2003
Covariant policy search JA Bagnell, J Schneider Carnegie Mellon University, 2003	226	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
Deep learning with sets and point clouds S Ravanbakhsh, J Schneider, B Poczos arXiv preprint arXiv:1611.04500, 2016	209	2016
Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly K Kandasamy, KR Vysyaraju, W Neiswanger, B Paria, CR Collins, ... Journal of Machine Learning Research 21 (81), 1-27, 2020	208	2020
On the error of random Fourier features DJ Sutherland, J Schneider arXiv preprint arXiv:1506.02785, 2015	207	2015
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	205	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	178	2016

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