Conformal prediction for plant recognition: long tailed and multiple inputs

Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. The two works I will present, introduce refinements to these methods for classification tasks, one specifically tailored for long-tailed classification and, the other one, 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 such as Pl@ntnet, where the distribution of species has this behavior and where multiple images of the same plant or animal are captured by individuals at test time. These works have been done in collaboration with Joseph Salmon (UM), Tiffany Ding (UC Berkeley) and Mohamed Hebiri (Université Gustave Eiffel).