124 citations · 657 across the 40 of their papers we have counts for
10 papers · 1 filter
Model-X Sequential Testing for Conditional Independence via Testing by Betting
Shalev Shaer, Gal Maman, Yaniv Romano
This paper develops a model-free sequential test for conditional independence. The proposed test allows researchers to analyze an incoming i.i.d. data stream with any arbitrary dep…
Label Noise Robustness of Conformal Prediction
Bat-Sheva Einbinder, Shai Feldman, Stephen Bates +3
We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, c…
Semantic uncertainty intervals for disentangled latent spaces
Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates +2
Meaningful uncertainty quantification in computer vision requires reasoning about semantic information -- say, the hair color of the person in a photo or the location of a car on t…
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing
Jacopo Teneggi, Beepul Bharti, Yaniv Romano +1
The complex nature of artificial neural networks raises concerns on their reliability, trustworthiness, and fairness in real-world scenarios. The Shapley value -- a solution concep…
Learning to Increase the Power of Conditional Randomization Tests
Shalev Shaer, Yaniv Romano
The model-X conditional randomization test is a generic framework for conditional independence testing, unlocking new possibilities to discover features that are conditionally asso…
Coordinated Double Machine Learning
Nitai Fingerhut, Matteo Sesia, Yaniv Romano
Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with…
Fast Nonlinear Vector Quantile Regression
Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano +1
Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable given explanatory features .…
Achieving Risk Control in Online Learning Settings
Shai Feldman, Liran Ringel, Stephen Bates +1
To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk -- such as coverage o…
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia +1
Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate re…
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Anastasios N Angelopoulos, Amit P Kohli, Stephen Bates +5
Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that prot…