mlsanityData to 7 Oct 2026

Toward Explainable AI for Regression Models

arXiv 2112.11407 ↗First posted 21 Dec 2021cs.LG cs.AI stat.MLcited by 30

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Abstract (arXiv)

In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks. Gaining a better understanding is especially important e.g. for safety-critical ML applications or medical diagnostics etc. While such Explainable AI (XAI) techniques have reached significant popularity for classifiers, so far little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI for regression and classification tasks, establish novel theoretical insights and analysis for XAIR, provide demonstrations of XAIR on genuine practical regression problems, and finally discuss the challenges remaining for the field.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 51 of its 91 references that are in the map. First posted on arXiv 21 Dec 2021.

Builds on

51 of 91 references.

References · 51

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  7. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  8. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  9. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  10. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  11. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  12. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
38 more
  1. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  2. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  4. 2014Striving for Simplicity: The All Convolutional Netcited by 983
  5. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  6. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  7. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
  8. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  9. 2020Directional Message Passing for Molecular Graphscited by 556
  10. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  11. 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 416
  12. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
  13. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 342
  14. 2016Top-down Neural Attention by Excitation Backpropcited by 318
  15. 2019Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNetcited by 260
  16. 2018Learning to Explain: An Information-Theoretic Perspective on Model Interpretationcited by 248
  17. 2020Explaining by Removing: A Unified Framework for Model Explanationcited by 218
  18. 2019Explanations can be manipulated and geometry is to blamecited by 215
  19. 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
  20. 2020Machine Learning Force Fieldscited by 204
  21. 2016Synthesizing the preferred inputs for neurons in neural networks via deep generator networkscited by 181
  22. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 165
  23. 2020Higher-Order Explanations of Graph Neural Networks via Relevant Walkscited by 138
  24. 2019Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximationcited by 115
  25. 2019Machine learning for molecular simulationcited by 109
  26. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  27. 2017Detecting Statistical Interactions from Neural Network Weightscited by 80
  28. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 71
  29. 2018iNNvestigate neural networks!cited by 58
  30. 2019From Clustering to Cluster Explanations via Neural Networkscited by 34
  31. 2020Building and Interpreting Deep Similarity Modelscited by 34
  32. 2018Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Modelscited by 24
  33. 2019Explaining and Interpreting LSTMscited by 23
  34. 2021Counterfactual Explanations for Arbitrary Regression Modelscited by 22
  35. 2020How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networkscited by 18
  36. 2017Understanding and Comparing Deep Neural Networks for Age and Gender Classificationcited by 17
  37. 2019NeuralHydrology -- Interpreting LSTMs in Hydrologycited by 12
  38. 2019Learning Global Pairwise Interactions with Bayesian Neural Networkscited by 6

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Citing papers per year: 2022: 4; 2023: 10; 2024: 9; 2025: 5; 2026: 2.

30 papers.

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  1. 2024Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarkscited by 28
  2. 2023Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Sciencecited by 25
  3. 2024xMIL: Insightful Explanations for Multiple Instance Learning in Histopathologycited by 15
  4. 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UKcited by 13
  5. 2025RelP: Faithful and Efficient Circuit Discovery in Language Models via Relevance Patchingcited by 10
  6. 2023Preemptively Pruning Clever-Hans Strategies in Deep Neural Networkscited by 9
  7. 2024Explaining Predictive Uncertainty by Exposing Second-Order Effectscited by 9
  8. 2025Towards Desiderata-Driven Design of Visual Counterfactual Explainerscited by 7
  9. 2023On the Soundness of XAI in Prognostics and Health Management (PHM)cited by 6
  10. 2023Calibrated Explanations for Regressioncited by 5
  11. 2022Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Seriescited by 4
  12. 2023An XAI framework for robust and transparent data-driven wind turbine power curve modelscited by 4
18 more
  1. 2022Automatic Identification of Chemical Moietiescited by 2
  2. 2023eXplainable Artificial Intelligence (XAI) in aging clock modelscited by 2
  3. 2023Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagationcited by 2
  4. 2024Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Datacited by 2
  5. 2026Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathologycited by 2
  6. 2022Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variablescited by 1
  7. 2024Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip frameworkcited by 1
  8. 2024Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attributioncited by 1
  9. 2024xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancercited by 1
  10. 2024Learning Visually Grounded Domain Ontologies via Embodied Conversation and Explanationcited by 1
  11. 2025A New Perspective On AI Safety Through Control Theory Methodologiescited by 1
  12. 2022XAI for transparent wind turbine power curve modelscited by 0
  13. 2023Metric Tools for Sensitivity Analysis with Applications to Neural Networkscited by 0
  14. 2023When a CBR in Hand is Better than Twins in the Bushcited by 0
  15. 2024XpertAI: uncovering regression model strategies for sub-manifoldscited by 0
  16. 2025Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learningcited by 0
  17. 2025TimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoringcited by 0
  18. 2026Localisation-Aware Uncertainty for Pretrained Object Detectioncited by 0