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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eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts
May 7, 2026Paulo Mario P. Medina, Jose Marie Antonio Miñoza, Sebastian C. IbañezDistribution Shift Robustness Interpretable ML
Center for AI Research
An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data
May 7, 2026Wing Yi Yu, Chun Yin ChiuClinical Decision Support Interpretable ML
School of Health Sciences, University of East Anglia, Norwich, United Kingdom · Department of Informatics, King's College London, London, United Kingdom
A renormalization-group inspired lattice-based framework for piecewise generalized linear models
May 6, 2026Joshua C. ChangInterpretable ML Statistical Learning Theory
NIH Clinical Center, Rehabilitation Medicine · Mederrata Research Inc · Sound Prediction Inc
Beyond Semantics: An Evidential Reasoning-Aware Multi-View Learning Framework for Trustworthy Mental Health Prediction
May 6, 2026Yucheng Ruan, Ling Huang, Qika Lin +2Evidential Deep Learning Uncertainty Quantification
Saw Swee Hock School of Public Health, National University of Singapore, Singapore
Agentic-imodels: Evolving agentic interpretability tools via autoresearch
May 5, 2026Chandan Singh, Yan Shuo Tan, Weijia Xu +4Interpretable ML Automated Machine Learning
Microsoft Research · National University of Singapore
ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
May 4, 2026Yuxi Cai, Lan Li, Feiqing Huang +1Interpretable ML Recurrent Neural Networks
Department of Statistics and Actuarial Science, University of Hong Kong
Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
May 4, 2026Jose Vinicius Ribeiro, Rafael Figueira Goncalves, Fabio Luiz Melquiades +1Feature Attribution Explainable Artificial Intelligence
Applied Nuclear Physics Laboratory · State University of Londrina · Dept. of Engineering and Architecture +2
Gradient Boosted Risk Scores
May 4, 2026Costa Georgantas, Jonas RichiardiInterpretable ML Clinical Prediction
†Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
May 1, 2026Shuyan Huang, Alexander Scarlatos, Jaewook Lee +1Intelligent Tutoring Systems Interpretable ML
University of Massachusetts Amherst
Knowing when to trust machine-learned interatomic potentials
May 1, 2026Shams Mehdi, Ilkwon Cho, Olexandr IsayevMachine Learning Interatomic Potentials Selective Prediction
Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA. · Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA. · Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA.
PrismAgent: Illuminating Harm in Memes via a Zero-Shot Interpretable Multi-Agent Framework
May 1, 2026Zihan Ding, Ziyuan Yang, Yi ZhangInterpretable ML
Sichuan University, Chengdu, Sichuan, China · Nanyang Technological University, Singapore
Benchmarking bandgap prediction in semiconductors under experimental and realistic evaluation settings
Apr 28, 2026Haolin Wang, Xianyuan Liu, Anna Jungbluth +3Benchmark Design Interpretable ML
Centre for Machine Intelligence, University of Sheffield, Sheffield, S1 3JD, UK · School of Computer Science, University of Sheffield, Sheffield, S1 4DP, UK · Climate Office, European Space Agency, Harwell, OX11 0FD, UK +2
RCProb: Probabilistic rule extraction from classification tree ensembles
Apr 28, 2026Josue ObregonProbability Calibration Interpretable ML
Department of Industrial Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul, 01811, Republic of Korea
Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions
Apr 27, 2026Hung N. Do, Jessica Z. Kubicek-Sutherland, Oscar A. Negrete +1Interpretable ML Protein-Protein Interaction Prediction
Theoretical Biology and Biophysics Group, Theoretical Division; 2Physical Chemistry and Applied Spectroscopy Group, Chemistry Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA 87545 · Physical Chemistry and Applied Spectroscopy Group, Chemistry Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA 87545 · Systems Biology Department, Sandia National Laboratories, Livermore, California, USA 94550
Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning
Apr 23, 2026Kaitlin Gili, Mainak Nistala, Kristen Wendell +1AI in Education Interpretable ML
Department of Computer Science, Tufts University, Medford, MA, U.S.A. · Departments of Mechanical Engineering and Education, Tufts University, Medford, MA, U.S.A.
Verifying Machine Learning Interpretability Requirements through Provenance
Apr 23, 2026Lynn Vonderhaar, Juan Couder, Daryela Cisneros +1Interpretable ML
Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach, USA
Interpretable Quantile Regression by Optimal Decision Trees
Apr 22, 2026Valentin Lemaire, Gaël Aglin, Siegfried NijssenQuantile Regression Interpretable ML
Euranova, Rue Emile Francqui, Mont-Saint-Guibert, Belgium · Université Catholique de Louvain, Louvain-la-Neuve, Belgium
Improving clinical interpretability of linear neuroimaging models through feature whitening
Apr 22, 2026Sara Petiton, Antoine Grigis, Raphaël Vock +1Medical Diagnosis Interpretable ML
University Paris-Saclay, CEA, CNRS, NeuroSpin, Baobab UMR 9027, Saclay, France
Mechanistic Interpretability Tool for AI Weather Models
Apr 22, 2026Kirsten I. Tempest, Matthias Beylich, George C. CraigInterpretable ML Mechanistic Interpretability
Meteorological Institute Munich, Ludwig-Maximilians-Universität, 80333 Munich, Germany · Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Germany
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
Apr 22, 2026Weizhi Nie, Zhen Qu, Weijie Wang +4Interpretable ML LLM-Based Time Series Forecasting
School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, Tianjin, China · Department of Information Engineering and Computer Science, University of Trento, Trento, Trentino, Italy · Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, Jiangsu, China +3
Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference
Apr 21, 2026François RemyInterpretable ML Multi-Vector Retrieval
Parallia AI
ParamBoost: Gradient Boosted Piecewise Cubic Polynomials
Apr 20, 2026Nicolas Salvadé, Tim HillelInterpretable ML
Department of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT,, United Kingdom
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues
Apr 20, 2026Prajwal Vijay Kajare, Priyanshu Priya, Bikash Santra +1Automated Negotiation CoT Reasoning
School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, India · Department of Computer Science and Engineering, Indian Institute of Technology Patna, India
Federated Rule Ensemble Method in Medical Data
Apr 20, 2026Ke Wan, Kensuke Tanioka, Toshio ShimokawaInterpretable ML Privacy-Preserving ML
Department of Medical Data Science Wakayama Medical University · Department of Biomedical Sciences and Informatics Doshisha University · Department of Biostatistics Wakayama Medical University
Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains
Apr 18, 2026Dongkyu Cho, Xiyue Li, Samrachana Adhikari +1Concept Bottleneck Models Healthcare
Computer Science Department New York University New York, NY, USA · Department of Population Health NYU Grossman School of Medicine New York, NY, USA · School of Global Public Health Computer Science Department New York University New York, NY, USA
Prototype-Grounded Concept Models for Verifiable Concept Alignment
Apr 17, 2026Stefano Colamonaco, David Debot, Pietro Barbiero +1Concept Bottleneck Models Interpretable ML
Department of Computer Science, KU Leuven · IBM Research, Zurich
Discovering quantum phenomena with Interpretable Machine Learning
Apr 17, 2026Paulin de Schoulepnikoff, Hendrik Poulsen Nautrup, Hans J. Briegel +1Interpretable ML Quantum Machine Learning
University of Innsbruck, Department for Theoretical Physics, Technikerstr. 21a, A-6020 Innsbruck, Austria
EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs
Apr 17, 2026David BerghausEvent Time Prediction Time Series Imputation
Lamarr Institute · Fraunhofer IAIS
An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules
Apr 17, 2026Jingke Chen, Jingrui Zhong, Tazneen Hossain Tani +3Protein-Ligand Binding Affinity Prediction Interpretable ML
MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Beijing Frontier Research Center for Biological Structure, School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
Structural interpretability in SVMs with truncated orthogonal polynomial kernels
Apr 16, 2026Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. HuertasInterpretable ML Support Vector Machines
Dpto. de Biociencias, Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Madrid, C/ Tajo, s/n, 28670 Villaviciosa de Odón, Madrid, Spain. · Dpto. de Matemáticas, Facultad de Ciencias, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 7, 28049 Madrid, Spain. · Instituto de Ciencias Matemáticas (ICMAT), Campus de Cantoblanco UAM, C/ Nicolás Cabrera, 13–15, 28049 Madrid, Spain. +1

May 7, 2026, cs.CVJ/K move · Enter open · S save

eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts

Paulo Mario P. Medina, Jose Marie Antonio Miñoza, Sebastian C. Ibañez

Center for AI Research

Distribution Shift Robustness Interpretable ML Spurious Correlation Robustness

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Despite extensive research into mitigating distribution shifts, many existing algorithms yield inconsistent performance, often failing to outperform baseline Empirical Risk Minimization (ERM) across diverse scenarios and necessitating newer algorithms which can handle scenarios where existing algorithms currently underperform. Furthermore, high algorithmic complexity frequently limits interpretability and offers only an indirect means of addressing spurious correlations. We propose eXplaining to Learn (eX2L): an interpretable, explanation-based framework that decorrelates confounding features from a classifier's latent representations during training. eX2L achieves this by penalizing the similarity between Grad-CAM activation maps generated by a primary label classifier and those from a concurrently trained confounder classifier. On the rigorous Spawrious Many-to-Many Hard Challenge synthetic data benchmark, eX2L achieves an average accuracy (AA) of 82.24% +/- 3.87% and a worst-group accuracy (WGA) of 66.31% +/- 8.73%, outperforming the current state-of-the-art (SOTA) by 5.49% and 10.90%, respectively. Beyond its competitive performance, eX2L demonstrates that functional domain invariance can be enforced by explicitly decoupling label and nuisance attributes at the group level.

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