Interpretable ML

ML: Machine Learning

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19 papers in the last four weeks, up 58% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 206

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Interpretable AI with Local Distillation
Aug 24, 2026Erin Craig, Yiling Huang, Snigdha PanigrahiSurrogate Modeling Interpretable ML
Department of Biostatistics, University of Michigan · Department of Statistics, University of Michigan
J-Miner: Recovering the Decision Logic of Fine-Tuned LLM Classifiers as Compact Rules
Aug 17, 2026Yunfan Gao, Xinyi Huang, Tao Sheng +3LLM Interpretability Interpretable ML
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University · Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence, Fudan University · Meituan +1
On the global feature importance for interpretable and trustworthy heat demand forecasting
Aug 13, 2026Milan ZdravkovićFeature Attribution Interpretable ML
Faculty of Mechanical Engineering University of Niš ul. Aleksandra Medvedeva 14, 18000 Niš, Serbia
HyperANFIS: Enhancing Rule Representation and Interpretability in Adaptive Neuro-Fuzzy Systems via Hyperbolic Geometry
Aug 12, 2026Haoran Pei, Zhao Su, Zetao Lin +6Interpretable ML Fuzzy Logic
school of Information Science and Engineering, Lanzhou University · Department of Data Science and AI, Monash University · School of Computing and Information Technology, University of Wollongong +1
Robust Ambiguity Detection (RAD) From Model- and Feature-Space Consistency
Aug 12, 2026Manya Singh, Mark T. Keane, Arjun PakrashiSelective Prediction Interpretable ML
School of Computer Science, University College Dublin, Dublin, Ireland
How Simple Can It Get? From Interpretable Equations to Readable Rules for Financial Decision Making
Aug 10, 2026Adia Lumadjeng, Ilker Birbil, Erman AcarInterpretable ML Explainability Evaluation
University of Amsterdam Amsterdam, the Netherlands
Beyond the Black Box: Interpretable Models of Human Randomisation Failures
Aug 7, 2026Ngoc Linh DaoInterpretable ML Zero-Sum Games
Alpen-Adria-University of Klagenfurt, Austria
Evidential Rule Learning for Interpretable Classification with Abstention
Aug 6, 2026Javier Fumanal-Idocin, Javier Andreu-PerezSelective Prediction Interpretable ML
School of Computer Science and Electronic Engineering University of Essex, Colchester CO4 3SQ, UK
From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction
Aug 5, 2026Esra Zihni, Katryna Cisek, Hamzah Ziadeh +3Interpretable ML Clinical Outcome Prediction
Artificial Intelligence in Digital Health and Medicine, Technological University Dublin, Ireland · Aalborg University, Aalborg, Denmark · International Clinical Research Center, St. Anne’s University Hospital, Brno, Czechia +2
UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics
Aug 4, 2026Carson J. Cook, Ahmed J. Zerouali, Anthony Schmidt +3Latent Variable Models Interpretable ML
Amplify Education, Inc.
TravKAN: Fast and Interpretable Nonlinear Traversability Analysis with Kolmogorov-Arnold Networks
Aug 3, 2026Daniel Fusaro, Simone Mosco, Wanmeng Li +1Mobile Robotics Robot Navigation
Department of Information Engineering, University of Padova, Italy.
Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes
Aug 2, 2026Aoting Zeng, Kai Wang, Jianwei Wang +3Unsupervised Learning Interpretable ML
Shanghai Jiao Tong University Shanghai, China · University of New South Wales Sydney, Australia
xMICD: Explainable Representation of Multiple ICD Codes
Aug 2, 2026Pat Vatiwutipong, Kumkup Keeratisiwakul, Albert Phuoc Kien Van Truong +4Representation Learning Interpretable ML
Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand · Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany · Department of Transdisciplinary Science and Engineering, School of Environment and Society, Institute of Science Tokyo, Tokyo, Japan +3
Pretrain on Small Synthetic Data, Scale Large for Free: Symmetry-Aware Foundation Model for Logic Rule Induction
Aug 1, 2026Yin Jun PhuaInterpretable ML Equivariant Neural Networks
Institute of Science Tokyo
A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease
Jul 31, 2026Ranveer Singh, Pranuthi Tenali, Saurabh Mathur +6Alzheimer's Disease Interpretable ML
The University of Texas at Dallas, Richardson, TX · TU Darmstadt, Germany · Swarthmore College, PA +1
Information Bottleneck Learning for Faithful Time Series Forecasting Explanations
Jul 30, 2026Xu Zheng, Wei Cheng, Zhuomin Chen +3Multivariate Time Series Forecasting Explainable Time Series Forecasting
Florida International University, Miami, FL, USA · NEC Laboratories America, Princeton, NJ, USA · University of Houston, Houston, TX, USA +1
Expanding Data-Agnostic Pivotal Instances Selection Models with Proximity Trees and Ensemble Learning
Jul 29, 2026Alessio Cascione, Mattia Setzu, Cristiano Landi +2Interpretable ML
Department of Computer Science, University of Pisa, Largo B. Pontecorvo, Pisa, 56127, PI, Italy. · ISTI-CNR, Via G. Moruzzi, Pisa, 56127, PI, Italy.
SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
Jul 28, 2026Tamar Gozlan, Claudia V. GoldmanInterpretable ML Interpretability in RL
Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem · Hebrew University Business School, Data Science Department, The Hebrew University of Jerusalem
Interpretable Column Annotation with LLM-Symbolized Decision Process Materialization
Jul 28, 2026Mengqi Wang, Jianwei Wang, Qing Liu +5LLM-Assisted Annotation Interpretable ML
UNSW Sydney · University of New South Wales Sydney, NSW, Australia · Data61, CSIRO +4
A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings
Jul 28, 2026Debasmita Bhattacharya, Siying Ding, Alayna Nguyen +1Code-Switching Interpretable ML
Department of Computer Science, Columbia University · Instagram
Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes
Jul 24, 2026Athanasios Papastathopoulos-Katsaros, Steven T. Lee, Lin Yao +4Electroencephalography Interpretable ML
Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA · Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA · Department of Child Neurology and Developmental Neurosciences, Texas Children’s Hospital, Houston, TX, USA +3
Explainable quantum-compressed machine learning for complex fluid flows
Jul 23, 2026Xiao Xue, Maida Wang, Mingyang Gao +2Neural Surrogate Modeling Interpretable ML
Centre for Computational Science, University College London, London, UK · Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities, Boltzmannstraße 1, 85748 Garching, Germany · Centre for Advanced Research Computing, University College London, London, UK
Explaining Weather Bulletins via ILP
Jul 23, 2026Enrico Santi, Alessandro Dal Palù, Agostino Dovier +2Interpretable ML
University of Udine, DMIF · University of Udine, DMIF, Udine, Italy · University of Parma, SMFI +1
Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library
Jul 22, 2026Cayan Deniz Kucuktopana, Javier Fumanal-Idocin, Richard Pitts +1Interpretable ML Fuzzy Logic
School of Computer Science and Electronic Engineering University of Essex Colchester, United Kingdom
PhaseAware: Interpretable Human-in-the-Loop Rehabilitation Scoring with Boundary Monitoring
Jul 22, 2026Yankai Zheng, Yuhe Liu, Yuxin Ma +8Interpretable ML Human-in-the-Loop Evaluation
Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China · Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China · School of Future Technology, Xi'an Jiaotong University, Xi'an, 710049, China +2
Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
Jul 20, 2026Sazan Mahbub, Caleb N. Ellington, Zhiyuan Li +4Zero-Shot Learning Meta-Learning
Carnegie Mellon University · GenBio AI · University of Wisconsin–Madison +2
Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis
Jul 19, 2026Maede Azani Hassan Abadi, Shouyi WangInterpretable ML Air Quality Prediction
Department of Industrial, Manufacturing, & Systems Engineering, The University of Texas at Arlington
Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization
Jul 16, 2026Antony Garcia, Adrian Noriega, Gabrielle Britton +1Interpretable ML Clinical Risk Prediction
Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA · Facultad de Ingeniería Eléctrica, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama · Massachusetts Institute of Technology, Cambridge, MA 02139, USA +4
A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems
Jul 16, 2026Christoph Jürgen Hemmer, Florian Plaswig, Daniel DurstewitzDynamical Systems Zero-Shot Time Series Forecasting
Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany · Interdisciplinary Center for Scientific Computing (IWR), Heidelberg, Germany · Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations
Jul 14, 2026George V. Jose, Thao Liang Chiam, Toby Hughes +4Prototype-Based Classification Point Cloud Classification
Adelaide Dental School, Adelaide University, Adelaide, South Australia, Australia · Australian Institute for Machine Learning, Adelaide University, Adelaide, South Australia, Australia · School of Public Health, Adelaide University, Adelaide, South Australia, Australia

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Interpretable AI with Local Distillation

Erin Craig, Yiling Huang, Snigdha Panigrahi

Department of Biostatistics, University of Michigan · Department of Statistics, University of Michigan

Surrogate Modeling Interpretable ML Knowledge Distillation

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Modern AI models such as tabular foundation models and gradient-boosted ensembles can outpredict classical methods, but provide little basis for reasoning about their predictions. High-stakes decisions call for models that are both accurate and interpretable as built. Local linear modeling offers a path forward: a smooth regression function is locally well approximated by a linear one, allowing a linear fit near each query point to achieve high accuracy without sacrificing transparency. The challenges lie in learning what is "local" and developing statistical tools for interpretation. Here, we propose local distillation, in which a black-box "teacher" guides a regularized linear "student" model at each query point. The teacher (1) defines locality by upweighting training observations with similar predicted outcomes, and (2) anchors the fit with its prediction at the query point, included as a pseudo-observation whose weight is estimated from the data. For interpretation, we add a small amount of Gaussian randomization to the local objective and use refits to assess stability: selection frequencies identify reliable features at a query point, and clustering the randomized fits identifies stable subgroups across the data. Under the lasso penalty, we prove that this randomization yields feature-selection probabilities that are stable under small perturbations of the training responses. Across 17 benchmark datasets, local distillation nearly matches its AI teacher's accuracy while producing a sparse linear model at each test point. In a high-dimensional cancer gene expression example, the framework identifies patient subgroups whose local models use different genes; this heterogeneity is invisible to a global linear model, and difficult to surface in a black-box model.

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