Barnabas Poczos

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Citations	11966	10666
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Jeff SchneiderCarnegie Mellon UniversityVerified email at cs.cmu.edu
Kirthevasan KandasamyUniversity of California, BerkeleyVerified email at cs.cmu.edu
Sashank J. ReddiResearch Scientist, Google ResearchVerified email at cs.cmu.edu
Chun-Liang LiResearch Scientist, GoogleVerified email at google.com
Siamak RavanbakhshAssistant Professor, McGill UniversityVerified email at cs.mcgill.ca
Aarti SinghAssociate Professor of Machine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Lorincz, AndrasEotvos Lorand UniversityVerified email at inf.elte.hu
Alex SmolaAmazon Web ServicesVerified email at smola.org
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Zoltan SzaboProfessor, LSEVerified email at lse.ac.uk
Manzil ZaheerGoogle ResearchVerified email at cmu.edu
Junier OlivaAssistant Professor, University of North CarolinaVerified email at cs.unc.edu
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UVerified email at cs.cmu.edu
Shashank SinghMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Willie NeiswangerPostdoc, Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Suvrit SraMITVerified email at mit.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu

Barnabas Poczos

Associate professor, Carnegie Mellon University

Verified email at cs.cmu.edu - Homepage

Artificial Intelligence Machine Learning Statistics


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Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems 30, 2017	1415	2017
Mmd gan: Towards deeper understanding of moment matching network CL Li, WC Chang, Y Cheng, Y Yang, B Póczos Advances in neural information processing systems 30, 2017	542	2017
Bayesian optimization with robust bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in Neural Information Processing Systems, 4134-4142, 2016	500*	2016
Stochastic variance reduction for nonconvex optimization SJ Reddi, A Hefny, S Sra, B Poczos, A Smola International conference on machine learning, 314-323, 2016	499	2016
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh arXiv preprint arXiv:1810.02054, 2018	434	2018
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	401	2018
One network to solve them all--solving linear inverse problems using deep projection models JH Rick Chang, CL Li, B Poczos, BVK Vijaya Kumar, ... Proceedings of the IEEE International Conference on Computer Vision, 5888-5897, 2017	289	2017
High dimensional Bayesian optimisation and bandits via additive models K Kandasamy, J Schneider, B Póczos International conference on machine learning, 295-304, 2015	249	2015
Gradient descent learns one-hidden-layer cnn: Don’t be afraid of spurious local minima S Du, J Lee, Y Tian, A Singh, B Poczos International Conference on Machine Learning, 1339-1348, 2018	193	2018
Characterizing and avoiding negative transfer Z Wang, Z Dai, B Póczos, J Carbonell Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	191	2019
Gradient descent can take exponential time to escape saddle points SS Du, C Jin, JD Lee, MI Jordan, A Singh, B Poczos Advances in neural information processing systems 30, 2017	186	2017
On variance reduction in stochastic gradient descent and its asynchronous variants S J Reddi, A Hefny, S Sra, B Poczos, AJ Smola Advances in neural information processing systems 28, 2015	174	2015
Estimation of Rényi entropy and mutual information based on generalized nearest-neighbor graphs D Pál, B Póczos, C Szepesvári Advances in Neural Information Processing Systems 23, 2010	167	2010
Deep learning with sets and point clouds S Ravanbakhsh, J Schneider, B Poczos arXiv preprint arXiv:1611.04500, 2016	161	2016
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding F Lanusse, Q Ma, N Li, TE Collett, CL Li, S Ravanbakhsh, R Mandelbaum, ... Monthly Notices of the Royal Astronomical Society 473 (3), 3895-3906, 2018	147	2018
Parallelised Bayesian optimisation via Thompson sampling K Kandasamy, A Krishnamurthy, J Schneider, B Póczos International Conference on Artificial Intelligence and Statistics, 133-142, 2018	145	2018
Multi-fidelity bayesian optimisation with continuous approximations K Kandasamy, G Dasarathy, J Schneider, B Póczos International Conference on Machine Learning, 1799-1808, 2017	143	2017
Competence-based curriculum learning for neural machine translation EA Platanios, O Stretcu, G Neubig, B Poczos, TM Mitchell arXiv preprint arXiv:1903.09848, 2019	133	2019
Equivariance through parameter-sharing S Ravanbakhsh, J Schneider, B Poczos International Conference on Machine Learning, 2892-2901, 2017	130	2017
Learning to predict the cosmological structure formation S He, Y Li, Y Feng, S Ho, S Ravanbakhsh, W Chen, B Póczos Proceedings of the National Academy of Sciences 116 (28), 13825-13832, 2019	129	2019

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