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Class conditionnal conformal prediction for multiple inputs by p-value aggregation
Abstract
Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. The work I will present, introduces a refinement to these methods for classification tasks, specifically tailored for scenarios where multiple observations (multi-inputs) of a single instance are available at prediction time. Our approach is particularly motivated by applications in citizen science, where multiple images of the same plant or animal are captured by individuals. Our method integrates the information from each observation into conformal prediction, enabling a reduction in the size of the predicted label set while preserving the required class-conditional coverage guarantee. The approach is based on the aggregation of conformal p-values computed from each observation of a multi-input. By exploiting the exact distribution of these p-values, we propose a general aggregation framework using an abstract scoring function, encompassing many classical statistical tools. Knowledge of this distribution also enables refined versions of standard strategies, such as majority voting. The method is evaluated on simulated and real data, with a particular focus on Pl@ntNet, a citizen science platform that facilitates the collection and identification of plant species through user-submitted images. This work has been done in collaboration with Joseph Salmon (Université de Montpellier, Inria) and Mohamed Hebiri (Université Gustave Eiffel).
