2.1k citations · 3.1k across the 35 of their papers we have counts for
8 papers · 1 filter
Cross-Prediction-Powered Inference
Tijana Zrnic, Emmanuel J. Candès
While reliable data-driven decision-making hinges on high-quality labeled data, the acquisition of quality labels often involves laborious human annotations or slow and expensive s…
Conformal PID Control for Time Series Prediction
Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani
We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees. The algorithms we present b…
Model-free selective inference under covariate shift via weighted conformal p-values
Ying Jin, Emmanuel J. Candès
This paper introduces novel weighted conformal p-values and methods for model-free selective inference. The problem is as follows: given test units with covariates and missing…
Tight Distribution-Free Confidence Intervals for Local Quantile Regression
Jayoon Jang, Emmanuel Candès
It is well known that it is impossible to construct useful confidence intervals (CIs) about the mean or median of a response conditional on features without making stro…
Statistical Inference for Fairness Auditing
John J. Cherian, Emmanuel J. Candès
Before deploying a black-box model in high-stakes problems, it is important to evaluate the model's performance on sensitive subpopulations. For example, in a recidivism prediction…
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Kexin Huang, Ying Jin, Emmanuel Candès +1
Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their reliable de…