mlsanityData to 7 Oct 2026

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

arXiv 1711.06178 ↗First posted 16 Nov 2017stat.ML cs.LGcited by 68

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Abstract (arXiv)

The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Nov 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 19; 2022: 18; 2023: 14; 2024: 13; 2025: 3.

68 papers.

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  1. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  2. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 126
  3. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
  4. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  5. 2021Improving Deep Learning Interpretability by Saliency Guided Trainingcited by 68
  6. 2022Global Explainability of GNNs via Logic Combination of Learned Conceptscited by 64
  7. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 52
  8. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  9. 2021Defining and Quantifying the Emergence of Sparse Concepts in DNNscited by 36
  10. 2021Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recoverycited by 32
  11. 2023Does a Neural Network Really Encode Symbolic Concepts?cited by 27
  12. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
38 more
  1. 2023A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?cited by 24
  2. 2022Computing Abductive Explanations for Boosted Treescited by 23
  3. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 22
  4. 2021DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applicationscited by 18
  5. 2024CausalFormer: An Interpretable Transformer for Temporal Causal Discoverycited by 17
  6. 2022Neural Networks are Decision Treescited by 16
  7. 2021Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanationscited by 15
  8. 2021Explainable Activity Recognition for Smart Home Systemscited by 14
  9. 2024A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecastingcited by 14
  10. 2018Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networkscited by 13
  11. 2022Attributable Visual Similarity Learningcited by 12
  12. 2022Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVcited by 12
  13. 2022Learning Interpretable, High-Performing Policies for Autonomous Drivingcited by 11
  14. 2021Learning Optimal Predictive Checklistscited by 10
  15. 2023Concept Distillation: Leveraging Human-Centered Explanations for Model Improvementcited by 9
  16. 2023Interpretability-Aware Vision Transformercited by 8
  17. 2024Tree-Based Leakage Inspection and Control in Concept Bottleneck Modelscited by 8
  18. 2021Feature Importance Explanations for Temporal Black-Box Modelscited by 7
  19. 2022A Survey of Neural Treescited by 7
  20. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  21. 2024A Survey of Explainable Knowledge Tracingcited by 7
  22. 2021Explainable Diabetic Retinopathy Detection and Retinal Image Generationcited by 6
  23. 2022DT+GNN: A Fully Explainable Graph Neural Network using Decision Treescited by 6
  24. 2024Learning Electromagnetic Metamaterial Physics With ChatGPTcited by 6
  25. 2024Logical Distillation of Graph Neural Networkscited by 6
  26. 2023Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosiscited by 4
  27. 2021Improving Compositionality of Neural Networks by Decoding Representations to Inputscited by 2
  28. 2021Learning Conditional Invariance through Cycle Consistencycited by 2
  29. 2021Tree-based Focused Web Crawling with Reinforcement Learningcited by 2
  30. 2023SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Trainingcited by 2
  31. 2024CA-Stream: Attention-based pooling for interpretable image recognitioncited by 2
  32. 2024Understanding with toy surrogate models in machine learningcited by 2
  33. 2022Explainable multi-class anomaly detection on functional datacited by 1
  34. 2022NN2Rules: Extracting Rule List from Neural Networkscited by 1
  35. 2022EiHi Net: Out-of-Distribution Generalization Paradigmcited by 1
  36. 2023MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformercited by 1
  37. 2023Rule-Extraction Methods From Feedforward Neural Networks: A Systematic Literature Reviewcited by 1
  38. 2024Axe the X in XAI: A Plea for Understandable AIcited by 1

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