• Corpus ID: 246294455

Model Agnostic Interpretability for Multiple Instance Learning

@article{Early2022ModelAI,
  title={Model Agnostic Interpretability for Multiple Instance Learning},
  author={Joseph Early and Christine Evers and Sarvapali D. Ramchurn},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.11701}
}
In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared… 
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