Model agnostic interpretability for multiple instance learning

Early, Joseph, Evers, Christine and Ramchurn, Sarvapali (2022) Model agnostic interpretability for multiple instance learning. International Conference on Learning Representations 2022. 25 - 29 Apr 2022. 25 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

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 against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.

Text

2201.11701v2 - Accepted Manuscript

Available under License University of Southampton Accepted Manuscript Licence.

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More information

e-pub ahead of print date: 27 January 2022

Additional Information: 25 pages (9 content, 2 acknowledgement + references, 14 appendix). 16 figures (3 main content, 13 appendix). Submitted and accepted to ICLR 22, see http://openreview.net/forum?id=KSSfF5lMIAg . Revision: added additional acknowledgements

Venue - Dates: International Conference on Learning Representations 2022, 2022-04-25 - 2022-04-29

Keywords: cs.LG, cs.AI

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