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 | 796 | 2017 |
Conformalized quantile regression Y Romano, E Patterson, E Candes Advances in neural information processing systems 32, 2019 | 515 | 2019 |
RAISR: rapid and accurate image super resolution Y Romano, J Isidoro, P Milanfar IEEE Transactions on Computational Imaging 3 (1), 110-125, 2016 | 331 | 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 | 312 | 2017 |
Classification with valid and adaptive coverage Y Romano, M Sesia, E Candes Advances in Neural Information Processing Systems 33, 3581-3591, 2020 | 232 | 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 | 162 | 2017 |
Boosting of Image Denoising Algorithms Y Romano, M Elad arXiv preprint arXiv:1502.06220, 2015 | 158 | 2015 |
Deep knockoffs Y Romano, M Sesia, E Candès Journal of the American Statistical Association 115 (532), 1861-1872, 2020 | 154 | 2020 |
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 | 147 | 2014 |
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 | 143 | 2018 |
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 | 142 | 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 | 119 | 2023 |
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 | 107 | 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 | 77 | 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 | 75 | 2020 |
Conformal prediction using conditional histograms M Sesia, Y Romano Advances in Neural Information Processing Systems 34, 6304-6315, 2021 | 60 | 2021 |
Improving K-SVD denoising by post-processing its method-noise Y Romano, M Elad 2013 IEEE International Conference on Image Processing, 435-439, 2013 | 47 | 2013 |
Unified single-image and video super-resolution via denoising algorithms A Brifman, Y Romano, M Elad IEEE Transactions on Image Processing 28 (12), 6063-6076, 2019 | 43 | 2019 |
Achieving equalized odds by resampling sensitive attributes Y Romano, S Bates, E Candes Advances in neural information processing systems 33, 361-371, 2020 | 42 | 2020 |
Improving conditional coverage via orthogonal quantile regression S Feldman, S Bates, Y Romano Advances in neural information processing systems 34, 2060-2071, 2021 | 36 | 2021 |