mlsanityData to 14 Oct 2026

Cardinality-Minimal Explanations for Monotonic Neural Networks

arXiv 2205.09901 ↗First posted 19 May 2022cs.LGcited by 8

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Abstract (arXiv)

In recent years, there has been increasing interest in explanation methods for neural model predictions that offer precise formal guarantees. These include abductive (respectively, contrastive) methods, which aim to compute minimal subsets of input features that are sufficient for a given prediction to hold (respectively, to change a given prediction). The corresponding decision problems are, however, known to be intractable. In this paper, we investigate whether tractability can be regained by focusing on neural models implementing a monotonic function. Although the relevant decision problems remain intractable, we can show that they become solvable in polynomial time by means of greedy algorithms if we additionally assume that the activation functions are continuous everywhere and differentiable almost everywhere. Our experiments suggest favourable performance of our algorithms.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 30 of its 57 references that are in the map. First posted on arXiv 19 May 2022.

Builds on

30 of 57 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

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

8 papers.

  1. 2025Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanationscited by 13
  2. 2025Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasonscited by 12
  3. 2026Formal Mechanistic Interpretability: Automated Circuit Discovery with Provable Guaranteescited by 11
  4. 2024Even-if Explanations: Formal Foundations, Priorities and Complexitycited by 4
  5. 2026Unifying Formal Explanations: A Complexity-Theoretic Perspectivecited by 2
  6. 2023Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposalcited by 1
  7. 2026Provably Explaining Neural Additive Modelscited by 1
  8. 2023CIDR: A Cooperative Integrated Dynamic Refining Method for Minimal Feature Removal Problemcited by 0