In this paper, we propose a novel approach to tackle the multiple instance
regression (MIR) problem. This problem arises when the data is a collection of
bags, where each bag is made of multiple instances corresponding to the same
unique real-valued label. Our goal is to train a regression model which maps
the instances of an unseen bag to its unique label.