24
May
Vanderbilt Machine Learning Seminar Talk “Conformal Prediction under Ambiguous Ground Truth” Last week, I presented our work on Monte Carlo conformal prediction — conformal prediction with ambiguous and uncertain ground truth — at the Vanderbilt Machine Learning Seminar Series. In this work, we show how to adapt standard conformal prediction if there are no unique ground truth labels available due to disagreement among experts during annotation. In this article, I want to share the slides of my talk. Abstract Conformal Prediction (CP) allows to perform rigorous uncertainty quantification by constructing a prediction set $C(X)$ satisfying $mathbb{P}_{agg}(Y in C(X))geq 1-alpha$ for…
