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DAVIDSTUTZ

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ARTICLE

International Seminar on Distribution-Free Statistics Talk “Conformal Training: Learning Optimal Conformal Classifiers”

Last week, I had the please to give a talk at the recently started Seminar on Distribution-Free Statistics organized by Anastasios Angelopoulos. Specifically, I talked about conformal training, a procedure allowing to train a classifier and conformal predictor end-to-end. This allows to optimize arbitrary losses defined directly on the confidence sets obtained through conformal prediction and can be shown to improve inefficiency and other metrics for any conformal predictor used at test time. In this article, I want to share the corresponding recording.

Abstract

Modern deep learning based classifiers show very high accuracy on test data but this does not provide sufficient guarantees for safe deployment, especially in high-stake AI applications such as medical diagnosis. Usually, predictions are obtained without a reliable uncertainty estimate or a formal guarantee. Conformal prediction (CP) addresses these issues by using the classifier's probability estimates to predict confidence sets containing the true class with a user-specified probability. However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets. Thus, this paper explores strategies to differentiate through CP during training with the goal of training model with the conformal wrapper end-to-end. In our approach, conformal training (ConfTr), we specifically "simulate" conformalization on mini-batches during training. We show that ConfTr outperforms state-of-the-art CP methods for classification by reducing the average confidence set size (inefficiency). Moreover, it allows to "shape" the confidence sets predicted at test time, which is difficult for standard CP. On experiments with several datasets, we show ConfTr can influence how inefficiency is distributed across classes, or guide the composition of confidence sets in terms of the included classes, while retaining the guarantees offered by CP.

Paper covered:
David Stutz, Krishnamurthy (Dj)Dvijotham, Ali Taylan Cemgil, Arnaud Doucet. Learning Optimal Conformal Classifiers. ArXiv, 2021.

Recording

Also check out the webpage of the seminar as well as Anastasios Angelopoulos's webpage!

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