Barnabas Poczos

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Jeff SchneiderCarnegie Mellon UniversityAdresse e-mail validée de cs.cmu.edu
Kirthevasan KandasamyUniversity of Wisconsin-MadisonAdresse e-mail validée de cs.wisc.edu
Sashank J. ReddiResearch Scientist, Google ResearchAdresse e-mail validée de cs.cmu.edu
Siamak RavanbakhshAssistant Professor, McGill UniversityAdresse e-mail validée de cs.mcgill.ca
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityAdresse e-mail validée de cmu.edu
Alex SmolaBoson AIAdresse e-mail validée de smola.org
Chun-Liang LiResearch Scientist, AppleAdresse e-mail validée de apple.com
Lorincz, AndrasEotvos Lorand UniversityAdresse e-mail validée de inf.elte.hu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUAdresse e-mail validée de cs.cmu.edu
Shashank SinghMax Planck Institute for Intelligent SystemsAdresse e-mail validée de tuebingen.mpg.de
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UAdresse e-mail validée de cs.cmu.edu
Manzil ZaheerGoogle ResearchAdresse e-mail validée de cmu.edu
Junier OlivaAssociate Professor, University of North CarolinaAdresse e-mail validée de cs.unc.edu
Zoltan SzaboProfessor, LSEAdresse e-mail validée de lse.ac.uk
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonAdresse e-mail validée de cs.washington.edu
Willie NeiswangerAssistant Professor of Computer Science, University of Southern CaliforniaAdresse e-mail validée de cs.stanford.edu
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityAdresse e-mail validée de stat.cmu.edu
Bharath SriperumbudurPennsylvania State UniversityAdresse e-mail validée de psu.edu
Suvrit SraTUM and MITAdresse e-mail validée de mit.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstAdresse e-mail validée de cs.umass.edu

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Barnabas Poczos

Associate professor, Carnegie Mellon University

Adresse e-mail validée de cs.cmu.edu - Page d'accueil

Artificial Intelligence Machine Learning Statistics


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems 30, 2017	2847	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	849	2017
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh arXiv preprint arXiv:1810.02054, 2018	810	2018
Bayesian optimization with robust bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in Neural Information Processing Systems, 4134-4142, 2016	789*	2016
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	710	2018
Stochastic variance reduction for nonconvex optimization SJ Reddi, A Hefny, S Sra, B Poczos, A Smola International conference on machine learning, 314-323, 2016	675	2016
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	559	2019
High dimensional Bayesian optimisation and bandits via additive models K Kandasamy, J Schneider, B Póczos International conference on machine learning, 295-304, 2015	420	2015
Found in translation: Learning robust joint representations by cyclic translations between modalities H Pham, PP Liang, T Manzini, LP Morency, B Póczos Proceedings of the AAAI conference on artificial intelligence 33 (01), 6892-6899, 2019	408	2019
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	390	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	354	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	296	2017
Graph neural tangent kernel: Fusing graph neural networks with graph kernels SS Du, K Hou, RR Salakhutdinov, B Poczos, R Wang, K Xu Advances in neural information processing systems 32, 2019	292	2019
Parallelised Bayesian optimisation via Thompson sampling K Kandasamy, A Krishnamurthy, J Schneider, B Póczos International conference on artificial intelligence and statistics, 133-142, 2018	284	2018
Equivariance through parameter-sharing S Ravanbakhsh, J Schneider, B Poczos International conference on machine learning, 2892-2901, 2017	261	2017
Multi-fidelity bayesian optimisation with continuous approximations K Kandasamy, G Dasarathy, J Schneider, B Póczos International conference on machine learning, 1799-1808, 2017	255	2017
Point cloud gan CL Li, M Zaheer, Y Zhang, B Poczos, R Salakhutdinov arXiv preprint arXiv:1810.05795, 2018	254	2018
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	249	2019
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	247	2018
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	221	2020

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