Interpretable Machine Learning

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Twelve weeks of publication activity for this topic as it is defined today.

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Model Interpretability MethodsMechanistic Interpretability

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Period ending 2026-09-14

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A weekly snapshot of new work published in Interpretable Machine Learning.

55 papers

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  1. Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration

    Sep 14, 2026Yuning Wang, Iman Azimi, Amir M. Rahmani +1Clinical PredictionInterpretable Machine Learning

  2. Beliefs and Behavior in Language Models

    Sep 7, 2026Alex Smolin, Bryan WilderLarge Language Model ReliabilityBelief

  3. Beyond the Black Box: Interpretable Models of Human Randomisation Failures

    Aug 7, 2026Ngoc Linh DaoInterpretable Machine LearningGeneralizable Time-Series Behavioral Modeling

  4. Interpretable Machine Learning for Traffic Congestion Prediction: Unveiling the Impact of Different COVID-19 Periods

    Aug 2, 2026Dan Zhu, Chi Sin Ng, Litian Xie +1Accurate Traffic ForecastingCoronavirus Disease

  5. Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis

    Aug 2, 2026Jianglei Xing, Xiao Tan, Dongzhao Jin +3Shapley ValueInterpretable Machine Learning

  6. xMICD: Explainable Representation of Multiple ICD Codes

    Aug 2, 2026Pat Vatiwutipong, Kumkup Keeratisiwakul, Albert Phuoc Kien Van Truong +4International Classification Of Disease CodesInterpretable Machine Learning

  7. Search Strategies for Optimal Classification and Regression Trees

    Jul 30, 2026Jacobus G. M. van der Linden, Mim van den Bos, Emir DemirovićDecision TreesRandom Forest

  8. Expanding Data-Agnostic Pivotal Instances Selection Models with Proximity Trees and Ensemble Learning

    Jul 29, 2026Alessio Cascione, Mattia Setzu, Cristiano Landi +2Interpretable Machine LearningDecision Trees

  9. Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library

    Jul 22, 2026Cayan Deniz Kucuktopana, Javier Fumanal-Idocin, Richard Pitts +1Interpretable Machine LearningRegression

  10. For What Reason? Interpreting Models' Encoding of Causation and Antithesis

    Jul 20, 2026Abhidip Bhattacharyya, Shira WeinCausal Structure and ReasoningNext-Token Prediction

  11. Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare

    Jul 20, 2026Sazan Mahbub, Caleb Ellington, Zhiyuan Li +4Clinical PredictionInterpretable Machine Learning

  12. Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis

    Jul 19, 2026Maede Azani Hassan Abadi, Shouyi WangAir PollutionInterpretable Machine Learning

  13. A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance

    Jul 17, 2026Andrea FerrarioArtificial Intelligence GovernanceArtificial Intelligence Evaluation

  14. A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems

    Jul 16, 2026Christoph Jürgen Hemmer, Florian Plaswig, Daniel DurstewitzDynamical SystemsZero-Shot Forecasting

  15. Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again

    Jun 30, 2026Zakk Heile, Hayden McTavish, Margo Seltzer +1Decision TreesInterpretable Machine Learning

  16. t-STEP: An interpretable model for Total Electron Content predictions and irregularities estimations

    Jun 28, 2026Stephen Tete, Carl Shneider, Maxime Cordy +3Geomagnetic StormsInterpretable Machine Learning

  17. Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features

    Jun 26, 2026Aixa X. AndradeParkinsonClinical Prediction

  18. COCOLogic-V2: Identifying Logical Inconsistencies via Truly Hard-Negatives

    Jun 26, 2026David Steinmann, Antonia Wüst, Kristian Kersting +1Concept Bottleneck ModelsInterpretable Machine Learning

  19. FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks

    Jun 23, 2026Nicholas Majeske, Ariful AzadSpatio-Temporal ForecastingRetrieval-Augmented Forecasting

  20. Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting

    Jun 22, 2026Jinhao Li, Hao WangEnergy ForecastingKolmogorov-Arnold Networks

  21. How Transparent is DiffusionGemma?

    Jun 18, 2026Joshua Engels, Callum McDougall, Bilal Chughtai +11TransparencyLLM Reasoning Methods

  22. Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation

    Jun 16, 2026Soheun Yi, Yizhou Lu, Chandler Squires +1IdentifiabilityStructural Causal Model

  23. Can Physician Expertise Improve Machine Learning Identification of Delirium?

    Jun 16, 2026Xinyu Qin, Vicky Ye, Ruiheng Yu +1Clinical DecisionInterpretable Machine Learning

  24. Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules

    Jun 15, 2026Wei Xu, Ke Yang, Gang Luo +4Clinical PredictionInterpretable Machine Learning

  25. In Defense of Information Leakage in Concept-based Models

    Jun 9, 2026Mateo Espinosa ZarlengaClass-Incremental Concept Bottleneck ModelData Leakage

  26. Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution

    Jun 7, 2026Varun Khurana, Vijval Ekbote, Vashu Chauhan +3Feature EngineeringFeature Extraction

  27. ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation

    Jun 1, 2026David Rundel, Fabian Fumagalli, Maximilian Muschalik +2Shapley ValueBayesian Experimental Design

  28. Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model

    Jun 1, 2026Yijun Yang, Ruiqiang Xiao, Lijie Hu +4Medical Imaging DatasetsInterpretable Machine Learning

  29. Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods

    May 29, 2026Shasha Zhou, Mingyu Huang, Ke LiGenome-Wide Association StudyInterpretable Machine Learning

  30. Beyond Additive Decompositions: Interpretability Through Separability

    May 29, 2026Jinyang Liu, Munir Eberhardt HiabuInterpretable Machine LearningModel Interpretability Methods

  31. Learning to Feel Materials from Multisensory Tactile Data via Interpretable Models

    May 28, 2026Li Zou, Yasemin VardarTactile SensingRobotic Hands

  32. When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models

    May 27, 2026Ziba Jabbar Zare, Ulrich Aïvodji, Julien Ferry +1Interpretable Machine LearningModel Interpretability Methods

  33. When Interpretability Becomes a Liability: Adversarial Attacks on CBM Concept Layers

    May 25, 2026Aditya SridharConcept Bottleneck ModelsModel Interpretability Methods

  34. DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods

    May 21, 2026Maryia Zhyrko, Daisy Monika Lal, Erik van Mulligen +1Mental HealthPsychology

  35. INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification

    May 19, 2026Seongjun Lee, Seokhyun Lee, Changhee LeeTime-Series ClassificationStatistical Shape Model

  36. Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions

    May 18, 2026Yuwen Zeng, Dengzhe Hou, Zhang Zhang +4ElectroencephalogramInterpretable Machine Learning

  37. Interpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Data

    May 13, 2026Chuanchuan Sun, Zhen Yu, Qin Fan +2Chronic Kidney DiseaseArea Under The Receiver Operating Characteristic Curve

  38. Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations

    May 13, 2026Nicola Debole, Andrea Passerini, Stefano Teso +2Concept Bottleneck ModelsInterpretable Machine Learning

  39. OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting

    May 12, 2026Sanah Suri, Kieran Ringel, Maike SonnewaldClass-Incremental Concept Bottleneck ModelOcean Dynamics

  40. BoolXLLM: LLM-Assisted Explainability for Boolean Models

    May 12, 2026Du Cheng, Serdar Kadioglu, Xin WangExplainabilityInterpretable Machine Learning

  41. Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes

    May 11, 2026Kane Warrior, Dalia ChakrabartyGaussian ProcessAnisotropy

  42. Interpretable Machine Learning for Football Performance Analysis: Evidence of Limited Transferability from Elite Leagues to University Competition

    May 11, 2026Yu-Fang Tsai, Yu-Jen Chen, Kok-Hua Tan +5Interpretable Machine LearningSports Analytics

  43. Machine Collective Intelligence for Explainable Scientific Discovery

    Apr 30, 2026Gyoung S. Na, Chanyoung ParkArtificial Intelligence ScientistsScientific Discovery

  44. DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation

    Apr 29, 2026Junhu Fu, Ke Chen, Weidong Guo +9Medical Image GenerationAnatomy

  45. Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning

    Apr 23, 2026Kaitlin Gili, Mainak Nistala, Kristen Wendell +1Mechanistic InterpretabilityInterpretable Machine Learning

  46. Interpretable Quantile Regression by Optimal Decision Trees

    Apr 22, 2026Valentin Lemaire, Gaël Aglin, Siegfried NijssenQuantile RegressionInterpretable Machine Learning

  47. ParamBoost: Gradient Boosted Piecewise Cubic Polynomials

    Apr 20, 2026Nicolas Salvadé, Tim HillelGeneralized Linear ModelGradient Boosted Tree

  48. Discovering quantum phenomena with Interpretable Machine Learning

    Apr 17, 2026Paulin de Schoulepnikoff, Hendrik Poulsen Nautrup, Hans J. Briegel +1Quantum LearningQuantum Neural Networks

  49. Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoring

    Apr 16, 2026Shahar Cohen, David M. Steinberg, Yael Radzyner +1MissingnessInterpretable Machine Learning

  50. CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning

    Apr 16, 2026Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi +1Efficient Continual LearningContinual Learning

  51. Interpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations

    Dec 9, 2025Daniele Sorini, Sownak Bose, Mathilda Denison +1CosmologyMachine Learning

  52. Experiments with Optimal Model Trees

    Mar 17, 2025Sabino Francesco Roselli, Eibe FrankDecision TreesInterpretable Machine Learning

  53. Surrogate Modeling for Explainable Predictive Time Series Corrections

    Dec 27, 2024Alfredo Lopez, Florian SobieczkyTime Series ForecastingSurrogate Modeling

  54. MARS: A neurosymbolic approach for interpretable drug discovery

    Oct 2, 2024Lauren Nicole DeLong, Yojana Gadiya, Paola Galdi +2Drug DiscoveryNeurosymbolic Approach

  55. Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor

    Jul 31, 2022Caleb Princewill NwokochaInterpretable Machine LearningMachine Learning