mlsanityData to 7 Oct 2026

Model Agnostic Interpretability for Multiple Instance Learning

arXiv 2201.11701 ↗First posted 27 Jan 2022cs.LG cs.AIcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 27 Jan 2022.

Builds on

12 of 30 references.

References · 12

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  4. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  5. 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 416
  6. 2016Model-Agnostic Interpretability of Machine Learningcited by 219
  7. 2016Multiple Instance Learning: A Survey of Problem Characteristics and Applicationscited by 163
  8. 2020Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regressioncited by 153
  9. 2016Revisiting Multiple Instance Neural Networkscited by 119
  10. 2019Multiple instance learning with graph neural networkscited by 25
  11. 2018Learning and Interpreting Multi-Multi-Instance Learning Networkscited by 9
  12. 2021Nested Multiple Instance Learning with Attention Mechanismscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 4; 2025: 0; 2026: 3.

10 papers.

  1. 2024Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Surveycited by 52
  2. 2023Inherently Interpretable Time Series Classification via Multiple Instance Learningcited by 23
  3. 2024xMIL: Insightful Explanations for Multiple Instance Learning in Histopathologycited by 15
  4. 2022Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learningcited by 13
  5. 2024Multiple Instance Verificationcited by 2
  6. 2026Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathologycited by 2
  7. 2022Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classificationcited by 1
  8. 2024Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Propertycited by 0
  9. 2026Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathologycited by 0
  10. 2026CAR-MIL: Counterfactual Attention Regularization for Multiple Instance Learningcited by 0