M. Bashari*, Y. Lee*, R. Maor Lotan, E. Dobriban, and Y. Romano, Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees. *Equal Contribution. Code.
H. Davidov, G. Freidkin, S. Feldman, and Y. Romano, Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs. Code.
S. Feldman, S. Bates, and Y. Romano, Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting. Code.
M. Zaffran, J. Josse, Y. Romano, and A. Dieuleveut, Predictive Uncertainty Quantification with Missing Covariates.
L. Ringel*, E. Tolochinsky*, and Y. Romano, Learning a Continue-Thinking Token for Enhanced Test-Time Scaling. IJCNLP-AACL, 2025. Oral. *Equal Contribution. Code.
M. Bashari*, R. Maor Lotan*, Y. Lee*, E. Dobriban, and Y. Romano, Synthetic-Powered Predictive Inference. Advances in Neural Information Processing Systems (NeurIPS), 2025. *Equal Contribution. Code.
N. Shoham, R. Dorfman, S. Shaer, K. Y. Levy, and Y. Romano, Prediction-Powered Semi-Supervised Learning with Online Power Tuning. Advances in Neural Information Processing Systems (NeurIPS), 2025.
B. Einbinder, L. Ringel, and Y. Romano, Semi-Supervised Risk Control via Prediction-Powered Inference, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE-TPAMI), 2025. To appear.
S. Katz*, L. Ringel*, Y. Romano, and L. Wolf, Segment-Based Attention Masking for GPTs, Association for Computational Linguistics (ACL), 2025. *Equal Contribution. Code.
M. Bashari, M. Sesia, and Y. Romano, Robust Conformal Outlier Detection under Contaminated Reference Data, International Conference on Machine Learning (ICML), 2025. Code.
H. Davidov, S. Feldman, G. Shamai, R. Kimmel, and Y. Romano, Conformalized Survival Analysis for General Right-Censored Data, International Conference on Learning Representations (ICLR), 2025. Code.
Y. Bar*, S. Shaer*, and Y. Romano, Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach, Advances in Neural Information Processing Systems (NeurIPS), 2024. *Equal Contribution. Code.
S. Feldman and Y. Romano, Robust Conformal Prediction Using Privileged Information, Advances in Neural Information Processing Systems (NeurIPS), 2024. Code.
B. Einbinder*, S. Feldman*, S. Bates, A. N. Angelopoulos, A. Gendler, and Y. Romano, Label Noise Robustness of Conformal Prediction, Journal of Machine Learning Research (JMLR), 2024. *Equal Contribution.
N. Goldenstein, J. Sulam, and Y. Romano, Pivotal Auto-Encoder via Self-Normalizing ReLU, IEEE Transactions on Signal Processing (IEEE-TSP), 2024.
G. Yan, Y. Romano, and T. W. Weng, Provably Robust Conformal Prediction with Improved Efficiency, International Conference on Learning Representations (ICLR), 2024.
L. Ringel, R. Cohen, D. Freedman, M. Elad, and Y. Romano, Early Time Classification with Accumulated Accuracy Gap Control. International Conference on Machine Learning (ICML), 2024. Code.
M. Bashari*, A. Epstein*, Y. Romano*, and M. Sesia*, Derandomized Novelty Detection with FDR Control via Conformal E-values, Advances in Neural Information Processing Systems (NeurIPS), 2023. Code. *Alphabetical Order.
J. Teneggi*, B. Bharti*, Y. Romano, and J. Sulam, SHAP-XRT: The Shapley Value Meets Conditional Independence Testing, Transactions on Machine Learning Research (TMLR), 2023. *Equal Contribution.
S. Feldman, L. Ringel, S. Bates, and Y. Romano, Achieving Risk Control in Online Learning Settings, Transactions on Machine Learning Research (TMLR), 2023. Code.
G. Segal*, N. Keidar*, R. Maor Lotan*, Y. Romano, M. Herskovitz, and Y. Yaniv, Utilizing risk-controlling prediction calibration to reduce false alarm rates in epileptic seizure prediction, Frontiers in Neuroscience, 2023. *Equal Contribution.
O. Belhasin, Y. Romano, D. Freedman, E. Rivlin, M. Elad, Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023. Code.
Y. Romano*, H. Primack*, T. Vaknin*, I. Meirzada, I. Karpas, D. Furman, C. Tradonsky, and R. Ben Shlomi, Quantum Sparse Coding, Quantum Machine Intelligence, 2023. *Equal Contribution.
M. Zaffran, A. Dieuleveut, J. Josse, and Y. Romano, Conformal Prediction with Missing Values, International Conference on Machine Learning (ICML), 2023. Code.
S. Shaer*, G. Maman*, and Y. Romano, Model-X Sequential Testing for Conditional Independence via Testing by Betting, International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. *Equal Contribution. Code.
A. A. Rosenberg*, S. Vedula*, Y. Romano, and A. M. Bronstein, Fast Nonlinear Vector Quantile Regression, International Conference on Learning Representations (ICLR), 2023. *Equal Contribution. Code.
S. Bates*, E. J. Candès*, L. Lei*, Y. Romano*, and M. Sesia*, Testing for Outliers with Conformal p-values, Annals of Statistics, 2023. *Alphabetical Order. Code.
S. Shaer and Y. Romano, Learning to Increase the Power of Conditional Randomization Tests, Machine Learning, 2023. Code.
S. Feldman, S. Bates, and Y. Romano, Calibrated Multiple-Output Quantile Regression with Representation Learning, Journal of Machine Learning Research (JMLR), 2022. Code.
B. Einbinder*, Y. Romano*, M. Sesia*, and Y. Zhou*, Training Uncertainty-Aware Classifiers with Conformalized Deep Learning, Advances in Neural Information Processing Systems (NeurIPS), 2022. *Alphabetical Order. Code.
S. Sankaranarayanan, A. N. Angelopoulos, S. Bates, Y. Romano, and P. Isola, Semantic uncertainty intervals for disentangled latent spaces, Advances in Neural Information Processing Systems (NeurIPS), 2022. Code.
M. Scetbon*, L. Meunier*, and Y. Romano, An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings, International Conference on Machine Learning (ICML), 2022. *Equal Contribution. (Spotlight.) Code.
A. N. Angelopoulos*, A. P. Kohli*, S. Bates, M. I. Jordan, J. Malik, T. Alshaabi, S. Upadhyayula, and Y. Romano, Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging, International Conference on Machine Learning (ICML), 2022. *Equal Contribution. Code.
N. Fingerhut, M. Sesia, and Y. Romano, Coordinated Double Machine Learning, International Conference on Machine Learning (ICML), 2022. Code.
A. Gendler, T. W. Weng, L. Daniel, and Y. Romano, Adversarially Robust Conformal Prediction, International Conference on Learning Representations (ICLR), 2022. Code.
S. Li*, M. Sesia*, Y. Romano, E. J. Candès, and C. Sabatti, Searching for Consistent Associations with a Multi-Environment Knockoff Filter, Biometrika, 2021. *Equal Contribution. Code.
S. Feldman, S. Bates, and Y. Romano, Improving Conditional Coverage via Orthogonal Quantile Regression, Advances in Neural Information Processing Systems (NeurIPS), 2021. Code.
Y. Romano and M. Sesia, Conformal Prediction using Conditional Histograms, Advances in Neural Information Processing Systems (NeurIPS), 2021. (Spotlight.) Code.
Y. Romano, M. Elad, and P. Milanfar, A Denoiser Can Do Much More than Just Clean Noise, SIAM news, March 2021.
Y. Romano, S. Bates, and E. J. Candès, Achieving Equalized Odds by Resampling Sensitive Attributes, Advances in Neural Information Processing Systems (NeurIPS), 2020. Code.
Y. Romano*, M. Sesia*, and E. J. Candès, Classification with Valid and Adaptive Coverage, Advances in Neural Information Processing Systems (NeurIPS), 2020. *Equal Contribution. Project website. Code.
Y. Romano, R. F. Barber, C. Sabatti and E. J. Candès, With Malice Toward None: Assessing Uncertainty via Equalized Coverage, Harvard Data Science Review, 2020. Project website. Code.
Y. Romano*, M. Sesia*, and E. J. Candès, Deep Knockoffs, Journal of the American Statistical Association (JASA), 2019. *Equal Contribution. Project website. Code.
Y. Romano, E. Patterson, and E. J. Candès, Conformalized Quantile Regression, in Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 8-14, 2019. Project website. Code.
Y. Romano*, A. Averdam*, J. Sulam, and M. Elad, Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals, Journal of Mathematical Imaging and Vision, 2019. *Equal Contribution.
D. Simon, J. Sulam, Y. Romano, Y. Lue, and M. Elad, MMSE Approximation for Sparse Coding Algorithms Using Stochastic Resonance, in IEEE Trans. on Signal Processing, vol. 67, no. 17, pp. 4597-4610, 2019.
S. R. Peled, Y. Romano, and M. Elad, SOS Boosting for Image Deblurring Algorithms, in European Signal Processing Conference (EUSIPCO), A Coruña, Spain, September 2-6, 2019.
A. Brifman, Y. Romano, and M. Elad, Unified Single-Image and Video Super-Resolution via Denoising Algorithms, in IEEE Trans. on Image Processing, vol. 28, no. 12, pp. 6063-6076, 2019.
T. Hong, Y. Romano, and M Elad, Acceleration of RED via Vector Extrapolation, Journal of Visual Communication and Image Representation, vol. 63, 2019. Code.
V. Papyan, Y. Romano, J. Sulam, and M. Elad, Theoretical Foundations of Deep Learning via Sparse Representations, in IEEE Signal Processing Magazine, vol. 35, no. 4, pp. 72-89, 2018.
J. Sulam, V. Papyan, Y. Romano, and M. Elad, Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning, in IEEE Trans. on Signal Processing, vol. 66, no. 15, pp. 4090-4104, 2018.
J. Sulam, V. Papyan, Y. Romano, and M. Elad, Projecting onto the Multi-Layer Convolutional Sparse Coding Model, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Alberta, Canada, April 15-20, 2018.
P. Milanfar, and Y. Romano, Image Upscaling, US Patent 9,996,902, 2018.
Y. Romano, M. Elad, and P. Milanfar, RED-UCATION: A Novel CNN Architecture based on Denoising Nonlinearities, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Alberta, Canada, April 15-20, 2018.
V. Papyan*, Y. Romano*, and M. Elad, Convolutional Neural Networks Analyzed via Convolutional Sparse Coding, Journal of Machine Learning Research (JMLR), vol. 18, no. 1, pp. 2887-2938, 2017. *Equal Contribution.
Y. Romano, M. Elad, and P. Milanfar, The Little Engine that Could: Regularization by Denoising (RED), SIAM Journal on Imaging Sciences, vol. 10, no. 4, pp. 1804-1844, 2017. Code.
V. Papyan, Y. Romano, J. Sulam, and M. Elad, Convolutional Dictionary Learning via Local Processing, in IEEE International Conference on Computer Vision (ICCV), Venice, Italy, October 22-29, 2017. Code.
D. Batenkov, Y. Romano, and M. Elad, On the Global-Local Dichotomy in Sparsity Modeling, Compressed Sensing and its Applications, Springer International Publishing, 2017.
Y. Ren, Y. Romano, and M. Elad, Example-Based Image Synthesis via Randomized Patch-Matching, in IEEE Trans. on Image Processing, vol. 27, no. 1, pp. 220-235, 2017.
J. Sulam, Y. Romano, and R. Talmon, Dynamical System Classification with Diffusion Embedding for ECG-Based Person Identification, Signal Processing, Elsevier, vol. 130, pp. 403–411, 2017.
Y. Romano, J. Isidoro, and P. Milanfar, RAISR: Rapid and Accurate Image Super Resolution, in IEEE Trans. on Computational Imaging, vol. 3, no. 1, pp. 110-125, 2016. Supplementary Material. Unofficial implementation.
J. Sulam*, Y. Romano*, and M. Elad, Gaussian Mixture Diffusion, in IEEE International Conference on the Science of Electrical Engineering (ICSEE), Eilat, Israel, November 16-18, 2016. *Equal Contribution.
A. Brifman, Y. Romano, and M. Elad, Turning a Denoiser into a Super-Resolver using Plug and Play Priors, in IEEE International Conference on Image Processing (ICIP), Arizona, USA, September 25-28, 2016.
Y. Romano and M. Elad, Con-Patch: When a Patch Meets its Context, in IEEE Trans. on Image Processing, vol. 25, no. 9, pp. 3967-3978, 2016.
Y. Romano and M. Elad, Boosting of Image Denoising Algorithms, SIAM Journal on Imaging Sciences, vol. 8, No. 2, pp. 1187-1219, 2015.
Y. Romano and M. Elad, Patch-Disagreement as a Way to Improve K-SVD Denoising, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, April 19-24, 2015.
Y. Romano, M. Protter, and M. Elad, Single Image Interpolation via Adaptive Non-Local Sparsity-Based Modeling, in IEEE Trans. on Image Processing, vol. 23, no. 7, pp. 3085-3098, 2014.
Y. Romano and M. Elad, Improving K-SVD Denoising by Post-Processing its Method-Noise, in IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, September 15-18, 2013.