Interpretable ML

ML: Machine Learning

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Overview

Interpretable machine learning aims to make predictions understandable enough for people to inspect, challenge, and use, especially in high-stakes decisions and scientific discovery. Approaches include inherently transparent decision trees, rule sets, additive models, and symbolic formulas, as well as concept- and prototype-based models that connect predictions to human-readable ideas or examples. Other methods explain complex models after training with feature attributions, local surrogates, or analyses of internal representations, while newer work also builds in uncertainty, domain knowledge, and opportunities for human intervention. A central challenge is balancing predictive accuracy with explanations that are faithful, stable under changes in data, and meaningful to the people relying on them; evaluation increasingly tests these qualities rather than treating a plausible explanation as sufficient.

Latest papers

CardsList

310 papers26 papers in the last four weeks, level with the four weeks before. 0.2% of all new papers.

  1. Exploring the Potential of Symbolic Regression in Precision Livestock Farming

    Oct 29, 2026Elisabeth Mayrhuber, Maciej Oczak, Michael Iwersen +3Precision Livestock FarmingEvent Time Prediction

    Bioinformatics Research Group, PLFDoc, University of Applied Sciences Upper Austria, Hagenberg, Austria · Computer Vision Lab, TU Wien, Vienna, Austria · Precision Livestock Farming Hub, The University of Veterinary Medicine Vienna, Vienna, Austria +2

  2. Improving the Intrinsic Interpretability of Differentiable Decision Trees through Euclidean Feature-Concentration Regularisation

    Oct 29, 2026Qiqi YangInterpretable MLDecision Tree Learning

  3. Interpretable by Design Tabular Prediction via In-Context Local Linear Learning

    Oct 29, 2026Ran Eisenberg, Steven Berger, Ofir LindenbaumInterpretable MLTabular ICL

    Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel

  4. Interpretable Feature Discovery from Unstructured Data Using LLMs and Statistical Choice Models

    Oct 27, 2026Baptiste Arnaudo, Amine Bennouna, Sébastien MartinInterpretable MLLarge Language Model-Based Feature Engineering

    École polytechnique, Palaiseau, France · Now at the Institute for Computational and Mathematical Engineering (ICME), Stanford University. · Operations Department, Kellogg School of Management, Northwestern University, Evanston, IL, USA

  5. What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules

    Oct 22, 2026Tim Nico Bauerschmidt, Isabel Valera, Jilles VreekenNext-Event PredictionInterpretable ML

    Department of Computer Science, Saarland University · CISPA Helmholtz Center for Information Security

  6. Mastering Model Fit: Applications of the InterModel Vigorish for Interpretable Machine Learning

    Oct 21, 2026Jiani Yan, Pengyun Wang, Klint Kanopka +2Interpretable ML

    Leverhulme Centre for Demographic Science, University of Oxford · Nuffield College, University of Oxford · Data Science Institute, University of Chicago +2

  7. Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming

    Oct 21, 2026Gwen maudet, Mathis Da Cunha, Grégoire DanoyMixed-Integer Linear ProgrammingInterpretable ML

    SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg · FSTM/DCS, SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg

  8. Evidence Field: Adaptive Covering Programs for Medical Imaging

    Oct 17, 2026Isarar Siddique, Raiyan Siddique, Arshi Niyaz +2Interpretable MLExplainable Medical Image Analysis

    Convolity AI Lab

  9. Interpretability of AI weather models via intermediate decoding

    Oct 16, 2026Matthias Beylich, Kirsten I. Tempest, Tobias Selz +1Interpretable MLNeural Network Interpretability

    Meteorological Institute, LMU Munich

  10. Beyond Latent Personalization: Integrating HRV-Conditioned Personalization via LLM for Interpretable Stress Detection

    Oct 16, 2026Wei-Hsiang Ma, Ya-Tse Wu, Hsing-Hang Chou +1Interpretable ML

    Institute of Communication Engineering, National Tsing Hua University, Taiwan · Department of Electrical Engineering, National Tsing Hua University, Taiwan

  11. Decomposing Brain-LLM Alignment with Interpretable Semantic Representations

    Oct 15, 2026Camila Kolling, Mariya TonevaNeural EncodingLLM Interpretability

    Max Planck Institute for Software Systems (MPI-SWS)

  12. Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming

    Oct 14, 2026Thomas Pammer, Johannes Fürnkranz, Markus SinnlBoolean Function LearningInterpretable ML

    Institute for Application-Oriented Knowledge Processing (FAW), Johannes Kepler University Linz, Austria · LIT AI Lab, Johannes Kepler University Linz, Austria · Institute of Business Analytics & Technology Transformation/JKU Business School, Johannes Kepler University Linz, Austria

  13. RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification

    Oct 11, 2026Yi Tian, Xiaohuan Chen, Yufei tang +5Respiratory Sound ClassificationWeakly Supervised Learning

    Fuyao University of Science and Technology (fyust.edu.cn) · Xiamen University (xmu.edu.cn)

  14. A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes

    Oct 10, 2026M Jawad, HV Gupta, MA Farmani +2HydrologyInterpretable ML

    Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, USA, 85721.

  15. Hide and Seek: HIDE Concepts to SEEK explanations for black box models

    Oct 9, 2026Bhanu Tokas, Rahul Nair, Rockwell Jackson +1Interpretable MLConcept-Based Explanations

    Arizona State University

  16. DisFace3DNet: Explainable Facial Attractiveness Prediction via 3D Component Disentanglement

    Oct 8, 2026Fenggui Rao, Yan Luximon, Jie ZhangInterpretable MLRepresentation Disentanglement

    School of Design, The Hong Kong Polytechnic University, Hong Kong, China · School of Design, The Hong Kong Polytechnic University, Hong Kong, China, and the Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, Hong Kong, China · Faculty of Applied Sciences, Macao Polytechnic University, Macau, China

  17. Temporally Interpretable Differentiable Decision Trees

    Oct 7, 2026Eisuke Hirota, Aarav Sane, Rohan PalejaInterpretable MLInterpretability in RL

    Department of Computer Science, Purdue University · Thomas Lord Department of Computer Science, University of Southern California

  18. Revisiting Explainable AI through Model-Independent Concept Dictionaries

    Oct 7, 2026Thomas Schnake, Doreen Schöppenthau, Alexander Meyer +3Explainable Artificial IntelligenceInterpretable ML

    Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Toronto, Canada · Vector Institute for Artificial Intelligence, Toronto, Canada · Institute for AI in Medicine (IKIM), Charité – Universitätsmedizin Berlin, Germany +5

  19. What the Sleeve Feels: Explainable Machine Learning for Textile Pressure-Based Postural Screening

    Oct 7, 2026Limon Bin Hossain, Md Sadib Rahman AnantaWearable SensingDomain Generalization

    Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1000, Bangladesh

  20. X-OPM: Explainable Automatic Digital On-Chip Power Modeling for Enhanced Robustness

    Oct 6, 2026Jingbo Jiang, Xizi Chen, Jian Peng +1Interpretable ML

    Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong · Huazhong Agricultural University, Wuhan, China

  21. SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning

    Oct 6, 2026Yikun Ou, Wei LiSepsis PredictionInterpretable ML

    School of Computer Science, University of Sydney Sydney, Australia

  22. Logic-Logit: A Logic-Based Approach to Choice Modeling

    Oct 4, 2026Shuhan Zhang, Wendi Ren, Shuang LiHuman Preference ModelingInterpretable ML

    CUHK-Shenzhen

  23. Evolving LLM-Generated Features for Interpretable Classification

    Oct 2, 2026Jack Butler, Zainab Afolabi, Nikita KozodoiInterpretable MLAutomated Feature Engineering

    Amazon Web Services

  24. Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals

    Oct 2, 2026Ilya Chekin, Vyacheslav Malyugin, Vladimir Chirkov +1Job-Candidate MatchingInterpretable ML

    BroutonLab · Curately

  25. DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction

    Oct 1, 2026Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal +2Molecular Property PredictionInterpretable ML

    1Indian Institute of Technology, Delhi, India.

  26. Auditable Algebraic Counting Field for Cryptic-Pocket Detection from Apo Structures

    Oct 1, 2026Shan Yu, Xuening WuInterpretable MLStructure-Based Drug Design

    Pfizer, Shanghai, China

  27. Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation

    Sep 29, 2026Spyros Rigas, Theofilos Papadopoulos, Georgios Alexandridis +1Parameter EstimationSurrogate Modeling

    Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Greece · School of Electrical and Computer Engineering, National Technical University of Athens, Greece

  28. LAURA: Knowledge Distillation for Interpretable Ambiguous Clause Identification in Legal Contracts

    Sep 29, 2026Amrita Singh, Aditya Joshi, Jiaojiao Jiang +1Legal NLPInterpretable ML

    School of Computer Science and Engineering University of New South Wales (UNSW), Sydney

  29. Factorized Scheduling Principle: Learning Interpretable and Transferable Policies via Structured Additive Functions

    Sep 29, 2026Hong Je-Gal, Hyun-Suk LeeInterpretable MLPolicy Optimization

    Department of Artificial Intelligence and Robotics, Sejong University, South Korea · Artificial Intelligence Robotics Institute (AIRI), Sejong University, South Korea

  30. Explainability from Training with Applications to TCR-Epitope Prediction

    Sep 28, 2026Jiarui Li, Zixiang Yin, Samuel Landry +2Interpretable MLNeural Network Interpretability

    Department of Computer Science, Tulane University · Department of Biochemistry and Molecular Biology, Tulane University School of Medicine