Romano, Yaniv

32 publications

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

NeurIPS 2025 Prediction-Powered Semi-Supervised Learning with Online Power Tuning Noa Shoham, Ron Dorfman, Shalev Shaer, Kfir Yehuda Levy, Yaniv Romano

ICML 2025 Robust Conformal Outlier Detection Under Contaminated Reference Data Meshi Bashari, Matteo Sesia, Yaniv Romano

NeurIPS 2025 Synthetic-Powered Predictive Inference Meshi Bashari, Roy Maor Lotan, Yonghoon Lee, Edgar Dobriban, Yaniv Romano

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

JMLR 2024 Label Noise Robustness of Conformal Prediction Bat-Sheva Einbinder, Shai Feldman, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler, Yaniv Romano

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

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

NeurIPS 2024 Robust Conformal Prediction Using Privileged Information Shai Feldman, Yaniv Romano

TMLR 2023 Achieving Risk Control in Online Learning Settings Shai Feldman, Liran Ringel, Stephen Bates, Yaniv Romano

JMLR 2023 Calibrated Multiple-Output Quantile Regression with Representation Learning Shai Feldman, Stephen Bates, Yaniv Romano

ICML 2023 Conformal Prediction with Missing Values Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano

ICMLW 2023 Continuous Vector Quantile Regression Sanketh Vedula, Irene Tallini, Aviv A. Rosenberg, Marco Pegoraro, Emanuele Rodolà, Yaniv Romano, Alexander Bronstein

NeurIPS 2023 Derandomized Novelty Detection with FDR Control via Conformal E-Values Meshi Bashari, Amir Epstein, Yaniv Romano, Matteo Sesia

ICLR 2023 Fast Nonlinear Vector Quantile Regression Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano, Alexander Bronstein

MLJ 2023 Learning to Increase the Power of Conditional Randomization Tests Shalev Shaer, Yaniv Romano

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

TMLR 2023 SHAP-XRT: The Shapley Value Meets Conditional Independence Testing Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam

NeurIPSW 2023 Volume-Oriented Uncertainty for Inverse Problems Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad

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

ICML 2022 An Asymptotic Test for Conditional Independence Using Analytic Kernel Embeddings Meyer Scetbon, Laurent Meunier, Yaniv Romano

ICML 2022 Coordinated Double Machine Learning Nitai Fingerhut, Matteo Sesia, Yaniv Romano

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

NeurIPS 2022 Semantic Uncertainty Intervals for Disentangled Latent Spaces Swami Sankaranarayanan, Anastasios Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola

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

NeurIPS 2021 Conformal Prediction Using Conditional Histograms Matteo Sesia, Yaniv Romano

NeurIPS 2021 Improving Conditional Coverage via Orthogonal Quantile Regression Shai Feldman, Stephen Bates, Yaniv Romano

NeurIPS 2020 Achieving Equalized Odds by Resampling Sensitive Attributes Yaniv Romano, Stephen Bates, Emmanuel Candes

NeurIPS 2020 Classification with Valid and Adaptive Coverage Yaniv Romano, Matteo Sesia, Emmanuel Candes

NeurIPS 2019 Conformalized Quantile Regression Yaniv Romano, Evan Patterson, Emmanuel Candes

ICCV 2017 Convolutional Dictionary Learning via Local Processing Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad

JMLR 2017 Convolutional Neural Networks Analyzed via Convolutional Sparse Coding Vardan Papyan, Yaniv Romano, Michael Elad