21 citations · 46 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets
Charles Lu, Anastasios N. Angelopoulos, Stuart Pomerantz
The regulatory approval and broad clinical deployment of medical AI have been hampered by the perception that deep learning models fail in unpredictable and possibly catastrophic w…
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…
Distribution-Free, Risk-Controlling Prediction Sets
Stephen Bates, Anastasios Angelopoulos, Lihua Lei +2
While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying learning systems in…