A fundamental statistical question is how to detect "outliers" amidst a multitude of "inlier" data points. This challenge has fueled interest in conformal inference, which provides a flexible framework for outlier detection that requires no parametric assumptions about the data distribution and accommodates the use of machine learning models. In this talk, we present a distribution-free method for outlier detection and enumeration with finite-sample error control. Our method is designed for scenarios where the presence of outliers can be detected even if their precise identification is difficult due to the sparsity and weakness of their signals. We build upon the latest developments in conformal inference, integrating classical ideas from
multiple testing, local power, and rank tests.
This is joint work with Chiara Gaia Magnani and Matteo Sesia.
Relevant paper: https://arxiv.org/pdf/2308.05534