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.

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Latest papers 206

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Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations
May 21, 2026Safwen Naimi, Wassim Bouachir, Guillaume-Alexandre Bilodeau +1Interpretable ML Suicide Risk Modeling
Universit´e T´ELUQ, QC, Canada · Polytechnique Montr´eal, QC, Canada · Universit´e du Qu´ebec `a Montr´eal, QC, Canada
Mitigating Label Bias with Interpretable Rubric Embeddings
May 20, 2026Calvin Isley, Johann D. Gaebler, Sharad GoelRepresentation Learning Interpretable ML
Harvard Kennedy School · Harvard University · Cambridge, MA 02139 +1
Interpretable Discriminative Text Representations via Agreement and Label Disentanglement
May 20, 2026Tong Wang, Yiqing Xu, Leo Yang YangDisentangled Representation Learning Interpretable ML
School of Management Yale University New Haven, CT, US, 06511 · Department of Accountancy, Economics and Finance Hong Kong Baptist University Kowloon, Hong Kong SAR
Interpretable Computer Vision for Defect Detection in X-ray Tomography of Aerospace SiC/SiC Composites
May 19, 2026Antonio Peña Corredor, Julien Lesseur, Romain Nunez +2Prototype-Based Classification Interpretable ML
SES · 1IRT Saint Exup´ery, Toulouse, France · 2Safran Ceramics, Le Haillan, France +1
A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
May 19, 2026Rui Huang, Lican HuangClass-Imbalanced Learning Interpretable ML
School of Basic Medicine, Hangzhou Normal University, No.2318, Yuhangtang Rd, Yuhang District, Hangzhou, 311121, Zhejiang, China. · Research Department, Hangzhou Domain Zones Technology Co.Ltd.,2026 200 Zhenhua Rd, West District, Hangzhou, 310030, Zhejiang, China.
INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification
May 19, 2026Seongjun Lee, Seokhyun Lee, Changhee LeeTime Series Classification Interpretable ML
Department of Artificial Intelligence, Korea University
Staging by the Book: Automatic Sleep Stage Classification Using Scoring Rules
May 19, 2026Emil Hardarson, Konstantin Popov, Sigridur Sigurdardottir +3Sleep Stage Classification Time Series Classification
Department of Computer Science, Reykjavik University, Reykjavik, Iceland · Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland · Department of Engineering, Reykjavik University, Reykjavik, Iceland +1
Learning Interpretable Point-Based Clinical Risk Scores via Direct Optimization
May 18, 2026Ying Cui, Albert M Li, Vivek Charu +3Interpretable ML Clinical Risk Prediction
Department of Biomedical Data Science, Stanford University · Decatur High School · Department of Pathology, Stanford University School of Medicine +1
Can machine learning for quantum-gas experiments be explainable?
May 18, 2026I. B. Spielman amd J. P. ZwolakInterpretable ML Scientific ML
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA · Department of Physics, University of Maryland, College Park, MD 20742, USA · Joint Quantum Institute, University of Maryland, College Park, MD 20742, USA
Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
May 18, 2026Chengpeng Hu, Yingqian Zhang, Hendrik BaierReinforcement Learning Interpretable ML
Eindhoven University of Technology, Eindhoven, the Netherlands · Centrum Wiskunde & Informatica, Amsterdam, the Netherlands
Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations
May 18, 2026Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa +1Feature Attribution Interpretable ML
EDF R&D, SINCLAIR Lab · Université de Toulouse · Sorbonne Université +2
Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection
May 18, 2026Hang-Cheng Dong, Guodong Liu, Dong Ye +1Interpretable ML Industrial Inspection
School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China · Harbin Institute of Technology Suzhou Research Institute, Suzhou, China
An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training
May 17, 2026Hung-Yue Suen, Kuo-En HungIntelligent Tutoring Systems Interpretable ML
Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan
On-Device Interpretable Tsetlin Machine-Based Intrusion Detection for Secure IoMT
May 15, 2026Rahul Jaiswal, Per-Arne Andersen, Linga Reddy Cenkeramaddi +2Network Intrusion Detection Interpretable ML
Department of ICT, University of Agder, Norway
Explainable Detection of Depression Status Shifts from User Digital Traces
May 14, 2026Loris Belcastro, Francesco Gervino, Fabrizio Marozzo +2Social Media Analysis Depression Detection
DIMES, University of Calabria, Ponte P. Bucci 41/C, 87036 Rende, Italy
AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
May 14, 2026Magdalena Proszewska, N. SiddharthGraph Neural Networks Interpretable ML
School of Informatics, University of Edinburgh
Optimal Pattern Detection Tree for Symbolic Rule-Based Classification
May 14, 2026Young-Chae Hong, Yangho ChenInterpretable ML Mixed-Integer QP
Amazon Seattle, USA
Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems
May 14, 2026Emilio Mastriani, Alessandro Costa, Federico Incardona +2Interpretable ML Predictive Maintenance
INAF, Osservatorio Astrofisico di Catania, Via S. Sofia 78, Catania, 95123, Italy
ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows
May 13, 2026Alvaro Lopez Pellicer, Plamen Angelov, Marwan Bukhari +3LLM Hallucination Mitigation Healthcare
School of Computing and Communications, Lancaster University · Lancaster Medical School, Lancaster University · PUC-Rio, Puc-Behring Institute for AI
INSIGHTS: Demonstration-Based Summaries of Time Series Predictors
May 13, 2026Bar Eini Porat, Rom Gutman, Uri Shalit +1Explainable Artificial Intelligence Interpretable ML
Technion Israel Institute of Technology · Tel-Aviv University
BoolXLLM: LLM-Assisted Explainability for Boolean Models
May 12, 2026Du Cheng, Serdar Kadioglu, Xin WangExplainable Artificial Intelligence Boolean Function Learning
AI Center of Excellence, Fidelity Investments, MA, USA · Department of Computer Science, Brown University, RI, USA
Native Explainability for Bayesian Confidence Propagation Neural Networks: A Framework for Trusted Brain-Like AI
May 12, 2026Georgios Makridis, Georgios Fatouros, John Soldatos +2Bayesian Neural Networks Explainable Artificial Intelligence
ExpertAI-Lux S.a r.l · University of Piraeus, Greece, Department of Digital Systems · University of Glasgow School of Computing Science
A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length
May 12, 2026Zeqiang Xian, Caihui Liu, Yong Zhang +3Interpretable ML Multiclass Classification
Department of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, Jiangxi, China · Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University, Ganzhou 341000, Jiangxi, China · Department of Computer Science and Technology, Tongji University, 201804 Shanghai, China +1
ASD-Bench: A Four-Axis Comprehensive Benchmark of AI Models for Autism Spectrum Disorder
May 11, 2026Shubhankit Singh, Hassan Shaikh, Kuldeep Raghuwanshi +1Probability Calibration Autism Spectrum Disorder
Research Commons AI · IIT Bombay · IIT Delhi
Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning
May 10, 2026Siyu Wu, Cong Xu, Wei ZhangRepresentation Learning Interpretable ML
1Shanghai Institute of AI Education, East China Normal University, Shanghai, 200241, China · 2Shool of Computer Sicence and Technology, East China Normal University, Shanghai, 200241, China
Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction
May 8, 2026Jingzhan Ge, Ajeeth Vellore, Ajinkya Palwe +5Interpretable ML Materials Property Prediction
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT 06269 USA · U.S. Army Combat Capabilities Development Command, Ground Vehicle Systems Center, Warren, MI 48397 USA
RelAgent: LLM Agents as Data Scientists for Relational Learning
May 8, 2026Xingyue Huang, Louis Tichelman, Jinwoo Kim +2Interpretable ML LLM Agents
University of Oxford · AITHYRA · TU Wien +1
Approximation-Free Differentiable Oblique Decision Trees
May 8, 2026Subrat Prasad Panda, Blaise Genest, Arvind EaswaranInterpretable ML Tabular ML
College of Computing and Data Science Nanyang Technological University Singapore
Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition
May 8, 2026Talha Ilyas, Deval Mehta, Zongyuan GeSkeleton-Based Action Recognition Logical Reasoning
Department of ECSE, Faculty of Engineering, Monash University, Australia · Department of DSAI, Faculty of Information Technology, Monash University, Australia · AIM for Health Lab, Faculty of Information Technology, Monash University, Australia
ProtoSSL: Self-Supervised Pretraining and Downstream Transfer for Projection-Based Prototype Models
May 7, 2026Steven Song, Sahil Sethi, Brett Beaulieu-Jones +1Self-Supervised Learning Self-Supervised Pre-Training
Department of Computer Science, University of Chicago, Chicago, IL 60637, USA · Center for Translational Data Science, University of Chicago, Chicago, IL 60637, USA · Medical Scientist Training Program, University of Chicago, Chicago, IL 60637, USA +3

May 21, 2026, cs.CVJ/K move · Enter open · S save

Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations

Safwen Naimi, Wassim Bouachir, Guillaume-Alexandre Bilodeau, Brian Mishara

Universit´e T´ELUQ, QC, Canada · Polytechnique Montr´eal, QC, Canada · Universit´e du Qu´ebec `a Montr´eal, QC, Canada

Interpretable ML Suicide Risk Modeling Video Surveillance

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Understanding and monitoring human behavior in metro stations play an important role in supporting suicide prevention efforts, where early identification of high-risk situations can enable timely intervention. This requires assessing suicide risk from a surveillance video by jointly reasoning about the behavior of each passenger, his/her spatial context, and temporal dynamics. However, this assessment using videos captured by surveillance cameras is challenging, as it demands accurate perception of human motion, understanding of platform geometry, and aggregation of heterogeneous behavioral cues over time. In this work, we formalize the task of Suicide Risk Assessment (SRA) in metro stations and introduce the first interpretable framework that addresses this challenge. Unlike approaches that focus on isolated subtasks or attempt to infer intent directly, our formulation assesses suicide risk from accumulated evidence by incorporating person tracking, activity recognition, semantic segmentation of the platform, and trajectory-driven risk heatmap modeling. By formalizing SRA as a distinct task and benchmarking a complete operational pipeline achieving 83.2% ROC-AUC on real surveillance data, this work highlights the complexity of suicide risk assessment and opens new directions for research on interpretable AI systems for social good.

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