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Journal Articles
- 2024
[j21]Omer Belhasin
, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad:
Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3321-3333 (2024)- [j20]Yaniv Romano, Harel Primack, Talya Vaknin, Idan Meirzada, Ilan Karpas, Dov Furman, Chene Tradonsky, Ruti Ben-Shlomi:
Quantum sparse coding. Quantum Mach. Intell. 6(1): 4 (2024) - [j19]Nelson Goldenstein, Jeremias Sulam, Yaniv Romano:
Pivotal Auto-Encoder via Self-Normalizing ReLU. IEEE Trans. Signal Process. 72: 3201-3212 (2024) - 2023
- [j18]Shai Feldman, Stephen Bates, Yaniv Romano:
Calibrated Multiple-Output Quantile Regression with Representation Learning. J. Mach. Learn. Res. 24: 24:1-24:48 (2023) - [j17]Shalev Shaer, Yaniv Romano:
Learning to increase the power of conditional randomization tests. Mach. Learn. 112(7): 2317-2357 (2023) - [j16]Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano:
Achieving Risk Control in Online Learning Settings. Trans. Mach. Learn. Res. 2023 (2023) - [j15]Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam:
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing. Trans. Mach. Learn. Res. 2023 (2023) - 2020
- [j14]Yaniv Romano, Aviad Aberdam, Jeremias Sulam, Michael Elad:
Adversarial Noise Attacks of Deep Learning Architectures: Stability Analysis via Sparse-Modeled Signals. J. Math. Imaging Vis. 62(3): 313-327 (2020) - 2019
- [j13]Tao Hong, Yaniv Romano, Michael Elad:
Acceleration of RED via vector extrapolation. J. Vis. Commun. Image Represent. 63 (2019) - [j12]Alon Brifman, Yaniv Romano, Michael Elad:
Unified Single-Image and Video Super-Resolution via Denoising Algorithms. IEEE Trans. Image Process. 28(12): 6063-6076 (2019) - [j11]Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad:
MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance. IEEE Trans. Signal Process. 67(17): 4597-4610 (2019) - 2018
- [j10]Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad:
Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks. IEEE Signal Process. Mag. 35(4): 72-89 (2018) - [j9]Yi Ren, Yaniv Romano, Michael Elad:
Example-Based Image Synthesis via Randomized Patch-Matching. IEEE Trans. Image Process. 27(1): 220-235 (2018) - [j8]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. IEEE Trans. Signal Process. 66(15): 4090-4104 (2018) - 2017
- [j7]Vardan Papyan, Yaniv Romano, Michael Elad:
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding. J. Mach. Learn. Res. 18: 83:1-83:52 (2017) - [j6]Yaniv Romano, Michael Elad, Peyman Milanfar:
The Little Engine That Could: Regularization by Denoising (RED). SIAM J. Imaging Sci. 10(4): 1804-1844 (2017) - [j5]Jeremias Sulam, Yaniv Romano, Ronen Talmon:
Dynamical system classification with diffusion embedding for ECG-based person identification. Signal Process. 130: 403-411 (2017) - [j4]Yaniv Romano, John Isidoro, Peyman Milanfar:
RAISR: Rapid and Accurate Image Super Resolution. IEEE Trans. Computational Imaging 3(1): 110-125 (2017) - 2016
- [j3]Yaniv Romano, Michael Elad:
Con-Patch: When a Patch Meets Its Context. IEEE Trans. Image Process. 25(9): 3967-3978 (2016) - 2015
- [j2]Yaniv Romano, Michael Elad:
Boosting of Image Denoising Algorithms. SIAM J. Imaging Sci. 8(2): 1187-1219 (2015) - 2014
- [j1]Yaniv Romano, Matan Protter, Michael Elad:
Single Image Interpolation Via Adaptive Nonlocal Sparsity-Based Modeling. IEEE Trans. Image Process. 23(7): 3085-3098 (2014)
Conference and Workshop Papers
- 2024
- [c25]Ge Yan, Yaniv Romano, Tsui-Wei Weng:
Provably Robust Conformal Prediction with Improved Efficiency. ICLR 2024 - [c24]Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano:
Early Time Classification with Accumulated Accuracy Gap Control. ICML 2024 - 2023
- [c23]Shalev Shaer, Gal Maman, Yaniv Romano:
Model-X Sequential Testing for Conditional Independence via Testing by Betting. AISTATS 2023: 2054-2086 - [c22]Shai Feldman, Bat-Sheva Einbinder, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano:
Conformal Prediction is Robust to Dispersive Label Noise. COPA 2023: 624-626 - [c21]Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano, Alexander M. Bronstein:
Fast Nonlinear Vector Quantile Regression. ICLR 2023 - [c20]Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano:
Conformal Prediction with Missing Values. ICML 2023: 40578-40604 - [c19]Meshi Bashari, Amir Epstein, Yaniv Romano, Matteo Sesia:
Derandomized novelty detection with FDR control via conformal e-values. NeurIPS 2023 - 2022
- [c18]Asaf Gendler, Tsui-Wei Weng, Luca Daniel, Yaniv Romano:
Adversarially Robust Conformal Prediction. ICLR 2022 - [c17]Anastasios N. Angelopoulos, Amit Pal Singh Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano:
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. ICML 2022: 717-730 - [c16]Nitai Fingerhut, Matteo Sesia, Yaniv Romano:
Coordinated Double Machine Learning. ICML 2022: 6499-6513 - [c15]Meyer Scetbon, Laurent Meunier, Yaniv Romano:
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings. ICML 2022: 19328-19346 - [c14]Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou:
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning. NeurIPS 2022 - [c13]Swami Sankaranarayanan, Anastasios Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola:
Semantic uncertainty intervals for disentangled latent spaces. NeurIPS 2022 - 2021
- [c12]Shai Feldman, Stephen Bates, Yaniv Romano:
Improving Conditional Coverage via Orthogonal Quantile Regression. NeurIPS 2021: 2060-2071 - [c11]Matteo Sesia, Yaniv Romano:
Conformal Prediction using Conditional Histograms. NeurIPS 2021: 6304-6315 - 2020
- [c10]Yaniv Romano, Stephen Bates, Emmanuel J. Candès:
Achieving Equalized Odds by Resampling Sensitive Attributes. NeurIPS 2020 - [c9]Yaniv Romano, Matteo Sesia, Emmanuel J. Candès:
Classification with Valid and Adaptive Coverage. NeurIPS 2020 - 2019
- [c8]Shahar Romem Peled, Yaniv Romano, Michael Elad:
SOS Boosting for Image Deblurring Algorithms. EUSIPCO 2019: 1-5 - [c7]Yaniv Romano, Evan Patterson, Emmanuel J. Candès:
Conformalized Quantile Regression. NeurIPS 2019: 3538-3548 - 2018
- [c6]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Projecting on to the Multi-Layer Convolutional Sparse Coding Model. ICASSP 2018: 6757-6761 - [c5]Yaniv Romano, Michael Elad, Peyman Milanfar:
RED-UCATION: A Novel CNN Architecture Based on Denoising Nonlinearities. ICASSP 2018: 6762-6766 - 2017
- [c4]Vardan Papyan, Yaniv Romano, Michael Elad, Jeremias Sulam:
Convolutional Dictionary Learning via Local Processing. ICCV 2017: 5306-5314 - 2016
- [c3]Alon Brifman, Yaniv Romano, Michael Elad:
Turning a denoiser into a super-resolver using plug and play priors. ICIP 2016: 1404-1408 - 2015
- [c2]Yaniv Romano, Michael Elad:
Patch-disagreement as away to improve K-SVD denoising. ICASSP 2015: 1280-1284 - 2013
- [c1]Yaniv Romano, Michael Elad:
Improving K-SVD denoising by post-processing its method-noise. ICIP 2013: 435-439
Informal and Other Publications
- 2024
- [i35]Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano:
Early Time Classification with Accumulated Accuracy Gap Control. CoRR abs/2402.00857 (2024) - [i34]Ge Yan, Yaniv Romano, Tsui-Wei Weng:
Provably Robust Conformal Prediction with Improved Efficiency. CoRR abs/2404.19651 (2024) - [i33]Roy Maor Lotan, Inbal Talgam-Cohen, Yaniv Romano:
Strategy-Proof Auctions through Conformal Prediction. CoRR abs/2405.12016 (2024) - [i32]Shai Feldman, Yaniv Romano:
Robust Conformal Prediction Using Privileged Information. CoRR abs/2406.05405 (2024) - [i31]Nelson Goldenstein, Jeremias Sulam, Yaniv Romano:
Pivotal Auto-Encoder via Self-Normalizing ReLU. CoRR abs/2406.16052 (2024) - [i30]Yarin Bar, Shalev Shaer, Yaniv Romano:
Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach. CoRR abs/2408.07511 (2024) - 2023
- [i29]Hod Wirzberger, Assaf Kalinski, Idan Meirzada, Harel Primack, Yaniv Romano, Chene Tradonsky, Ruti Ben-Shlomi:
Lightsolver challenges a leading deep learning solver for Max-2-SAT problems. CoRR abs/2302.06926 (2023) - [i28]Meshi Bashari, Amir Epstein, Yaniv Romano, Matteo Sesia:
Derandomized Novelty Detection with FDR Control via Conformal E-values. CoRR abs/2302.07294 (2023) - [i27]Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad:
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems. CoRR abs/2305.10124 (2023) - [i26]Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano:
Conformal Prediction with Missing Values. CoRR abs/2306.02732 (2023) - 2022
- [i25]Anastasios N. Angelopoulos, Amit P. S. Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano:
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. CoRR abs/2202.05265 (2022) - [i24]Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou:
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning. CoRR abs/2205.05878 (2022) - [i23]Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano, Alex M. Bronstein:
Fast Nonlinear Vector Quantile Regression. CoRR abs/2205.14977 (2022) - [i22]Nitai Fingerhut, Matteo Sesia, Yaniv Romano:
Coordinated Double Machine Learning. CoRR abs/2206.00885 (2022) - [i21]Shalev Shaer, Yaniv Romano:
Learning to Increase the Power of Conditional Randomization Tests. CoRR abs/2207.01022 (2022) - [i20]Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam:
From Shapley back to Pearson: Hypothesis Testing via the Shapley Value. CoRR abs/2207.07038 (2022) - [i19]Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola:
Semantic uncertainty intervals for disentangled latent spaces. CoRR abs/2207.10074 (2022) - [i18]Bat-Sheva Einbinder, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano:
Conformal Prediction is Robust to Label Noise. CoRR abs/2209.14295 (2022) - 2021
- [i17]Shai Feldman, Stephen Bates, Yaniv Romano:
Improving Conditional Coverage via Orthogonal Quantile Regression. CoRR abs/2106.00394 (2021) - [i16]Shai Feldman, Stephen Bates, Yaniv Romano:
Calibrated Multiple-Output Quantile Regression with Representation Learning. CoRR abs/2110.00816 (2021) - [i15]Meyer Scetbon, Laurent Meunier, Yaniv Romano:
An 𝓁p-based Kernel Conditional Independence Test. CoRR abs/2110.14868 (2021) - 2020
- [i14]Yaniv Romano, Stephen Bates, Emmanuel J. Candès:
Achieving Equalized Odds by Resampling Sensitive Attributes. CoRR abs/2006.04292 (2020) - 2019
- [i13]Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, Emmanuel J. Candès:
With Malice Towards None: Assessing Uncertainty via Equalized Coverage. CoRR abs/1908.05428 (2019) - 2018
- [i12]Tao Hong, Yaniv Romano, Michael Elad:
Acceleration of RED via Vector Extrapolation. CoRR abs/1805.02158 (2018) - [i11]Yaniv Romano, Michael Elad:
Classification Stability for Sparse-Modeled Signals. CoRR abs/1805.11596 (2018) - [i10]Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad:
Improving Pursuit Algorithms Using Stochastic Resonance. CoRR abs/1806.10171 (2018) - 2017
- [i9]Dmitry Batenkov, Yaniv Romano, Michael Elad:
On the Global-Local Dichotomy in Sparsity Modeling. CoRR abs/1702.03446 (2017) - [i8]Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad:
Convolutional Dictionary Learning via Local Processing. CoRR abs/1705.03239 (2017) - [i7]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. CoRR abs/1708.08705 (2017) - 2016
- [i6]Yaniv Romano, Michael Elad:
Con-Patch: When a Patch Meets its Context. CoRR abs/1603.06812 (2016) - [i5]Yaniv Romano, John Isidoro, Peyman Milanfar:
RAISR: Rapid and Accurate Image Super Resolution. CoRR abs/1606.01299 (2016) - [i4]Vardan Papyan, Yaniv Romano, Michael Elad:
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding. CoRR abs/1607.08194 (2016) - [i3]Yi Ren, Yaniv Romano, Michael Elad:
Example-Based Image Synthesis via Randomized Patch-Matching. CoRR abs/1609.07370 (2016) - [i2]Yaniv Romano, Michael Elad, Peyman Milanfar:
The Little Engine that Could: Regularization by Denoising (RED). CoRR abs/1611.02862 (2016) - 2015
- [i1]Yaniv Romano, Michael Elad:
SOS Boosting of Image Denoising Algorithms. CoRR abs/1502.06220 (2015)
Coauthor Index
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