Improving K-SVD denoising by post-processing its method-noise. - Yaniv Romano; Michael Elad (ICIP, 2013)

Single Image Interpolation Via Adaptive Nonlocal Sparsity-Based Modeling. - Yaniv Romano; Matan Protter; Michael Elad (IEEE Trans. Image Process., 2014)

Patch-disagreement as away to improve K-SVD denoising. - Yaniv Romano; Michael Elad (ICASSP, 2015)

SOS Boosting of Image Denoising Algorithms. - Yaniv Romano; Michael Elad (CoRR, 2015)

Boosting of Image Denoising Algorithms. - Yaniv Romano; Michael Elad (SIAM J. Imaging Sci., 2015)

Turning a denoiser into a super-resolver using plug and play priors. - Alon Brifman; Yaniv Romano; Michael Elad (ICIP, 2016)

Con-Patch: When a Patch Meets Its Context. - Yaniv Romano; Michael Elad (IEEE Trans. Image Process., 2016)

Convolutional Dictionary Learning via Local Processing. - Vardan Papyan; Yaniv Romano; Michael Elad; Jeremias Sulam (ICCV, 2017)

On the Global-Local Dichotomy in Sparsity Modeling. - Dmitry Batenkov; Yaniv Romano; Michael Elad (CoRR, 2017)

Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. - Jeremias Sulam; Vardan Papyan; Yaniv Romano; Michael Elad (CoRR, 2017)

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding. - Vardan Papyan; Yaniv Romano; Michael Elad (J. Mach. Learn. Res., 2017)

The Little Engine That Could: Regularization by Denoising (RED). - Yaniv Romano; Michael Elad; Peyman Milanfar (SIAM J. Imaging Sci., 2017)

Dynamical system classification with diffusion embedding for ECG-based person identification. - Jeremias Sulam; Yaniv Romano; Ronen Talmon (Signal Process., 2017)

RAISR: Rapid and Accurate Image Super Resolution. - Yaniv Romano; John Isidoro; Peyman Milanfar (IEEE Trans. Computational Imaging, 2017)

RED-UCATION: A Novel CNN Architecture Based on Denoising Nonlinearities. - Yaniv Romano; Michael Elad; Peyman Milanfar (ICASSP, 2018)

Projecting on to the Multi-Layer Convolutional Sparse Coding Model. - Jeremias Sulam; Vardan Papyan; Yaniv Romano; Michael Elad (ICASSP, 2018)

Classification Stability for Sparse-Modeled Signals. - Yaniv Romano; Michael Elad (CoRR, 2018)

Improving Pursuit Algorithms Using Stochastic Resonance. - Dror Simon; Jeremias Sulam; Yaniv Romano; Yue M. Lu; Michael Elad (CoRR, 2018)

Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks. - Vardan Papyan; Yaniv Romano; Jeremias Sulam; Michael Elad (IEEE Signal Process. Mag., 2018)

Example-Based Image Synthesis via Randomized Patch-Matching. - Yi Ren 005; Yaniv Romano; Michael Elad (IEEE Trans. Image Process., 2018)

Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. - Jeremias Sulam; Vardan Papyan; Yaniv Romano; Michael Elad (IEEE Trans. Signal Process., 2018)

SOS Boosting for Image Deblurring Algorithms. - Shahar Romem Peled; Yaniv Romano; Michael Elad (EUSIPCO, 2019)

Conformalized Quantile Regression. - Yaniv Romano; Evan Patterson; Emmanuel J. Candès (NeurIPS, 2019)

With Malice Towards None: Assessing Uncertainty via Equalized Coverage. - Yaniv Romano; Rina Foygel Barber; Chiara Sabatti; Emmanuel J. Candès (CoRR, 2019)

Acceleration of RED via vector extrapolation. - Tao Hong 006; Yaniv Romano; Michael Elad (J. Vis. Commun. Image Represent., 2019)

Unified Single-Image and Video Super-Resolution via Denoising Algorithms. - Alon Brifman; Yaniv Romano; Michael Elad (IEEE Trans. Image Process., 2019)

MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance. - Dror Simon; Jeremias Sulam; Yaniv Romano; Yue M. Lu; Michael Elad (IEEE Trans. Signal Process., 2019)

Achieving Equalized Odds by Resampling Sensitive Attributes. - Yaniv Romano; Stephen Bates; Emmanuel J. Candès (NeurIPS, 2020)

Classification with Valid and Adaptive Coverage. - Yaniv Romano; Matteo Sesia; Emmanuel J. Candès (NeurIPS, 2020)

Adversarial Noise Attacks of Deep Learning Architectures: Stability Analysis via Sparse-Modeled Signals. - Yaniv Romano; Aviad Aberdam; Jeremias Sulam; Michael Elad (J. Math. Imaging Vis., 2020)

Improving Conditional Coverage via Orthogonal Quantile Regression. - Shai Feldman; Stephen Bates; Yaniv Romano (NeurIPS, 2021)

Conformal Prediction using Conditional Histograms. - Matteo Sesia; Yaniv Romano (NeurIPS, 2021)

An 𝓁p-based Kernel Conditional Independence Test. - Meyer Scetbon; Laurent Meunier; Yaniv Romano (CoRR, 2021)

Adversarially Robust Conformal Prediction. - Asaf Gendler; Tsui-Wei Weng; Luca Daniel; Yaniv Romano (ICLR, 2022)

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. - Anastasios N. Angelopoulos; Amit Pal Singh Kohli; Stephen Bates; Michael I. Jordan; Jitendra Malik; Thayer Alshaabi; Srigokul Upadhyayula; Yaniv Romano (ICML, 2022)

Coordinated Double Machine Learning. - Nitai Fingerhut; Matteo Sesia; Yaniv Romano (ICML, 2022)

An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings. - Meyer Scetbon; Laurent Meunier; Yaniv Romano (ICML, 2022)

Training Uncertainty-Aware Classifiers with Conformalized Deep Learning. - Bat-Sheva Einbinder; Yaniv Romano; Matteo Sesia; Yanfei Zhou (NeurIPS, 2022)

Semantic uncertainty intervals for disentangled latent spaces. - Swami Sankaranarayanan; Anastasios Angelopoulos; Stephen Bates; Yaniv Romano; Phillip Isola (NeurIPS, 2022)

From Shapley back to Pearson: Hypothesis Testing via the Shapley Value. - Jacopo Teneggi; Beepul Bharti; Yaniv Romano; Jeremias Sulam (CoRR, 2022)

Conformal Prediction is Robust to Label Noise. - Bat-Sheva Einbinder; Stephen Bates; Anastasios N. Angelopoulos; Asaf Gendler; Yaniv Romano (CoRR, 2022)

Model-X Sequential Testing for Conditional Independence via Testing by Betting. - Shalev Shaer; Gal Maman; Yaniv Romano (AISTATS, 2023)

Conformal Prediction is Robust to Dispersive Label Noise. - Shai Feldman; Bat-Sheva Einbinder; Stephen Bates; Anastasios N. Angelopoulos; Asaf Gendler; Yaniv Romano (COPA, 2023)

Fast Nonlinear Vector Quantile Regression. - Aviv A. Rosenberg; Sanketh Vedula; Yaniv Romano; Alexander M. Bronstein (ICLR, 2023)

Conformal Prediction with Missing Values. - Margaux Zaffran; Aymeric Dieuleveut; Julie Josse; Yaniv Romano (ICML, 2023)

Derandomized novelty detection with FDR control via conformal e-values. - Meshi Bashari; Amir Epstein; Yaniv Romano; Matteo Sesia (NeurIPS, 2023)

Lightsolver challenges a leading deep learning solver for Max-2-SAT problems. - Hod Wirzberger; Assaf Kalinski; Idan Meirzada; Harel Primack; Yaniv Romano; Chene Tradonsky; Ruti Ben-Shlomi (CoRR, 2023)

Calibrated Multiple-Output Quantile Regression with Representation Learning. - Shai Feldman; Stephen Bates; Yaniv Romano (J. Mach. Learn. Res., 2023)

Achieving Risk Control in Online Learning Settings. - Shai Feldman; Liran Ringel; Stephen Bates; Yaniv Romano (Trans. Mach. Learn. Res., 2023)

SHAP-XRT: The Shapley Value Meets Conditional Independence Testing. - Jacopo Teneggi; Beepul Bharti; Yaniv Romano; Jeremias Sulam (Trans. Mach. Learn. Res., 2023)

Learning to increase the power of conditional randomization tests. - Shalev Shaer; Yaniv Romano (Mach. Learn., 2023)

Provably Robust Conformal Prediction with Improved Efficiency. - Ge Yan; Yaniv Romano; Tsui-Wei Weng (ICLR, 2024)

Early Time Classification with Accumulated Accuracy Gap Control. - Liran Ringel; Regev Cohen; Daniel Freedman; Michael Elad; Yaniv Romano (ICML, 2024)

Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach. - Yarin Bar; Shalev Shaer; Yaniv Romano (NeurIPS, 2024)

Robust Conformal Prediction Using Privileged Information. - Shai Feldman; Yaniv Romano (NeurIPS, 2024)

Strategy-Proof Auctions through Conformal Prediction. - Roy Maor Lotan; Inbal Talgam-Cohen; Yaniv Romano (CoRR, 2024)

Semi-Supervised Risk Control via Prediction-Powered Inference. - Bat-Sheva Einbinder; Liran Ringel; Yaniv Romano (CoRR, 2024)

Pivotal Auto-Encoder via Self-Normalizing ReLU. - Nelson Goldenstein; Jeremias Sulam; Yaniv Romano (IEEE Trans. Signal Process., 2024)

Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems. - Omer Belhasin; Yaniv Romano; Daniel Freedman; Ehud Rivlin; Michael Elad (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Quantum sparse coding. - Yaniv Romano; Harel Primack; Talya Vaknin; Idan Meirzada; Ilan Karpas; Dov Furman; Chene Tradonsky; Ruti Ben-Shlomi (Quantum Mach. Intell., 2024)

Segment-Based Attention Masking for GPTs. - Shahar Katz; Liran Ringel; Yaniv Romano; Lior Wolf (ACL (1), 2025)

Conformalized Survival Analysis for General Right-Censored Data. - Hen Davidov; Shai Feldman; Gil Shamai; Ron Kimmel; Yaniv Romano (ICLR, 2025)

Robust Conformal Outlier Detection under Contaminated Reference Data. - Meshi Bashari; Matteo Sesia; Yaniv Romano (CoRR, 2025)

Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting. - Shai Feldman; Stephen Bates; Yaniv Romano (CoRR, 2025)

Synthetic-Powered Predictive Inference. - Meshi Bashari; Roy Maor Lotan; Yonghoon Lee; Edgar Dobriban; Yaniv Romano (CoRR, 2025)

Learning a Continue-Thinking Token for Enhanced Test-Time Scaling. - Liran Ringel; Elad Tolochinsky; Yaniv Romano (CoRR, 2025)

Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs. - Hen Davidov; Gilad Freidkin; Shai Feldman; Yaniv Romano (CoRR, 2025)