Yaniv Romano

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Michael EladProfessor of Computer Science, Technion, IsraelVerified email at cs.technion.ac.il
Emmanuel CandesThe Simons Chair in Mathematics and Statistics, Stanford UniversityVerified email at stanford.edu
Matteo SesiaUniversity of Southern CaliforniaVerified email at usc.edu
Jeremias SulamAssistant Professor, BME, Johns Hopkins UniversityVerified email at jhu.edu
Stephen BatesAssistant Professor, MIT EECSVerified email at cs.berkeley.edu
Vardan PapyanAssistant Professor, University of TorontoVerified email at utoronto.ca
Peyman MilanfarDistinguished Scientist, GoogleVerified email at google.com
Shai FeldmanVerified email at cs.technion.ac.il
Anastasios Nikolas AngelopoulosPh.D. Student, UC BerkeleyVerified email at berkeley.edu
Evan PattersonTopos InstituteVerified email at epatters.org
Asaf GendlerResearch Engineer at Bosch Center for Artificial IntelligenceVerified email at campus.technion.ac.il
LIHUA LEIStanford UniversityVerified email at stanford.edu
Chiara SabattiProfessor of Biomedical Data Science and of Statistics, Stanford UniversityVerified email at stanford.edu
Matan ProtterBigPandaVerified email at bigpanda.io
Shalev ShaerPhD candidate, Technion, IsraelVerified email at campus.technion.ac.il
Srigokul UpadhyayulaUniversity of California, BerkeleyVerified email at berkeley.edu
Thayer AlshaabiHoward Hughes Medical Institute, University of California, BerkeleyVerified email at berkeley.edu
Amit Pal Singh KohliPhD Candidate, University of California, BerkeleyVerified email at berkeley.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Julie JosseSenior Researcher Inria,Verified email at inria.fr

Yaniv Romano

Assistant Professor of Electrical Engineering and Computer Science, Technion, Israel

Verified email at technion.ac.il - Homepage

Machine Learning Statistics Deep Learning Sparse Representations Inverse Problems


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The little engine that could: Regularization by denoising (RED) Y Romano, M Elad, P Milanfar SIAM Journal on Imaging Sciences 10 (4), 1804-1844, 2017	930	2017
Conformalized quantile regression Y Romano, E Patterson, E Candes Advances in neural information processing systems 32, 2019	664	2019
RAISR: rapid and accurate image super resolution Y Romano, J Isidoro, P Milanfar IEEE Transactions on Computational Imaging 3 (1), 110-125, 2016	346	2016
Convolutional neural networks analyzed via convolutional sparse coding V Papyan, Y Romano, M Elad Journal of Machine Learning Research 18 (83), 1-52, 2017	333	2017
Classification with valid and adaptive coverage Y Romano, M Sesia, E Candes Advances in Neural Information Processing Systems 33, 3581-3591, 2020	321	2020
Deep knockoffs Y Romano, M Sesia, E Candès Journal of the American Statistical Association 115 (532), 1861-1872, 2020	178	2020
Convolutional dictionary learning via local processing V Papyan, Y Romano, J Sulam, M Elad Proceedings of the IEEE International Conference on Computer Vision, 5296-5304, 2017	173	2017
Boosting of Image Denoising Algorithms Y Romano, M Elad arXiv preprint arXiv:1502.06220, 2015	168	2015
Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks V Papyan, Y Romano, J Sulam, M Elad IEEE Signal Processing Magazine 35 (4), 72-89, 2018	153	2018
Testing for outliers with conformal p-values S Bates, E Candès, L Lei, Y Romano, M Sesia The Annals of Statistics 51 (1), 149-178, 2023	152	2023
Multilayer convolutional sparse modeling: Pursuit and dictionary learning J Sulam, V Papyan, Y Romano, M Elad IEEE Transactions on Signal Processing 66 (15), 4090-4104, 2018	150	2018
Single image interpolation via adaptive nonlocal sparsity-based modeling Y Romano, M Protter, M Elad IEEE Transactions on Image Processing 23 (7), 3085-3098, 2014	150	2014
Turning a denoiser into a super-resolver using plug and play priors A Brifman, Y Romano, M Elad 2016 IEEE International Conference on Image Processing (ICIP), 1404-1408, 2016	114	2016
Image-to-image regression with distribution-free uncertainty quantification and applications in imaging AN Angelopoulos, AP Kohli, S Bates, M Jordan, J Malik, T Alshaabi, ... International Conference on Machine Learning, 717-730, 2022	106	2022
With malice toward none: Assessing uncertainty via equalized coverage Y Romano, RF Barber, C Sabatti, E Candès Harvard Data Science Review 2 (2), 4, 2020	91	2020
Conformal prediction using conditional histograms M Sesia, Y Romano Advances in Neural Information Processing Systems 34, 6304-6315, 2021	81	2021
Training uncertainty-aware classifiers with conformalized deep learning BS Einbinder, Y Romano, M Sesia, Y Zhou Advances in Neural Information Processing Systems 35, 22380-22395, 2022	51	2022
Achieving equalized odds by resampling sensitive attributes Y Romano, S Bates, E Candes Advances in neural information processing systems 33, 361-371, 2020	51	2020
Improving K-SVD denoising by post-processing its method-noise Y Romano, M Elad 2013 IEEE International Conference on Image Processing, 435-439, 2013	49	2013
Improving conditional coverage via orthogonal quantile regression S Feldman, S Bates, Y Romano Advances in neural information processing systems 34, 2060-2071, 2021	47	2021

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