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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xFODE: An Explainable Fuzzy Additive ODE Framework for System Identification
Apr 16, 2026Ertugrul Kececi, Tufan KumbasarInterpretable ML Nonlinear System Identification
AI and Intelligent Systems Laboratory, Istanbul Technical University, 34469, Istanbul, Türkiye
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 +1Interpretable ML
Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel · Israel NDC, Soreq Nuclear Research Center, Yavne, Israel
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
Apr 16, 2026Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi +1Class-Incremental Learning Concept Bottleneck Models
Department of Electrical and Computer Engineering, University of California San Diego · Department of Computer Science and Engineering, University of California San Diego · Halıcıoğlu Data Science Institute, University of California San Diego
Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit
Apr 5, 2026Wenhao Chi, Ş. İlker BirbilCost-Sensitive Learning Interpretable ML
Yau Mathematical Sciences Center, Tsinghua University · Amsterdam Business School, University of Amsterdam
Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints
Mar 26, 2026Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi +3Interpretable ML Physics-Informed ML
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA · Department of Geosciences, University of Arizona, Tucson, AZ, USA
When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews
Mar 25, 2026Hasindri Watawana, Sergio Burdisso, Diego A. Moreno-Galván +4Depression Detection Interpretable ML
Idiap Research Institute, Switzerland · EPFL, Switzerland · Centro de Investigación en Matemáticas (CIMAT), Mexico +1
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
Mar 8, 2026Hans Farrell Soegeng, Sarthak Ketanbhai Modi, Thomas PeyrinBoolean Function Learning Interpretable ML
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore.
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
Feb 10, 2026Zizheng Zhang, Yiming Li, Justin Xu +6Interpretable ML Clinical Prediction
Microsoft Research · University of Oxford
KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning
Feb 3, 2026Binbin Yong, Haoran Pei, Jun Shen +3Interpretable ML Fuzzy Logic
School of Information Science and Engineering, Lanzhou University, China · School of Computing and Information Technology, University of Wollongong, Australia · Department of Data Science and Artificial Intelligence, Monash University, Australia
Explainability of Complex AI Models with Correlation Impact Ratio
Jan 10, 2026Poushali Sengupta, Rabindra Khadka, Sabita Maharjan +5Feature Attribution Interpretable ML
Institute of Informatics, University of Oslo, Oslo, Norway · Department of Computer Science, Oslo Metropolitan University, Oslo, Norway · Aalborg University, Denmark
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
Nov 3, 2025Jacob CarlsonSelective Inference Interpretable ML
Harvard University∗
Interpretable Nanoporous Materials Design with Symmetry-Aware Networks
Sep 19, 2025Zhenhao Zhou, Salman Bin Kashif, Jin-Hu Dou +4Interpretable ML Materials Property Prediction
Center of Hydrogen Science and School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai 201203, China · Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States +3
Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises
Aug 30, 2025Lu Han, Xiuying WangFinancial Services Interpretable ML
School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China · School of Computer Science, University of Sydney, NSW 2006, Australia
Learning Interpretable Differentiable Logic Networks for Time-Series Classification
Aug 24, 2025Chang Yue, Niraj K. JhaTime Series Classification Interpretable ML
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
Learning thermodynamic master equations for open quantum systems
Jun 2, 2025Peter Sentz, Stanley Nicholson, Yujin Cho +3Quantum System Identification Interpretable ML
Department of Data and Decision Sciences, Emory University, Atlanta, GA 30322 · 2Division of Applied Mathematics, Brown University, Providence, RI 02912 · 3Lawrence Livermore National Laboratory, Livermore, CA 94550
Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance
Mar 27, 2025Markéta Krúpová, Nabil Rachdi, Quentin GuibertInterpretable ML
CEREMADE · 1ADDACTIS France, 46 bis Chemin du Vieux Moulin, 69160 Tassin-la-Demi-Lune, France · 2CEREMADE, Universit´e Paris-Dauphine, PSL University, CNRS, 75016 Paris, France
Experiments with Optimal Model Trees
Mar 17, 2025Sabino Francesco Roselli, Eibe FrankInterpretable ML Decision Tree Learning
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. · Department of Computer Science, University of Waikato, Hamilton, New Zealand.
MARS: A neurosymbolic approach for interpretable drug discovery
Oct 2, 2024Lauren Nicole DeLong, Yojana Gadiya, Paola Galdi +2Interpretable ML AI-Assisted Scientific Research
School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, United Kingdom. · Cancer Research UK Scotland Institute, Switchback Road, Glasgow, G61 1BD, United Kingdom. · Enveda Therapeutics, 5700 Flatiron Parkway, Boulder, 80301, Colorado, United States of America.
Interpretable Clustering: A Survey
Sep 1, 2024Lianyu Hu, Mudi Jiang, Junjie Dong +2Clustering Interpretable ML
College of Information Science and Engineering, Henan University of Technology, China · School of Software, Dalian University of Technology, China · Xinchang Power Supply Company, State Grid Corporation of China, China
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
Jul 30, 2024Yinlong Xu, Zitai Kong, Yixuan Wu +5Interpretable ML Electrocardiogram Classification
State Key Laboratory of Transvascular Implantation Devices and TIDRI, Zhejiang University, Hangzhou, China · School of Public Health, Zhejiang University and Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence, Hangzhu, China · State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University, Hangzhou, China +3
Probabilistic Truly Unordered Rule Sets
Jan 18, 2024Lincen Yang, Matthijs van LeeuwenInterpretable ML Multiclass Classification
LIACS, Leiden University Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields
Nov 29, 2023Francesco Spinnato, Riccardo Guidotti, Anna Monreale +1Time Series Classification Interpretable ML
Scuola Normale Superiore, Pisa, Italy · University of Pisa, Pisa, Italy · ISTI-CNR, Pisa, Italy
Topology-enhanced machine learning for speech signal processing
Nov 26, 2023Pingyao Feng, Qingrui Qu, Haiyu Zhang +4Interpretable ML Speech Processing
Department of Mathematics, Southern University of Science and Technology, Shenzhen, China. · Department of Mathematics, North Carolina State University, Raleigh, NC, USA. · School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China. +1
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
Apr 21, 2023Shuyin Xia, Guoyin Wang, Xinbo Gao +2Interpretable ML Adaptive Computation
Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, and Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China · College of Computer and Information Science, the National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor
Jul 31, 2022Caleb Princewill NwokochaInterpretable ML Incremental Learning
Deep Learning for Individual Heterogeneity
Oct 28, 2020Max H. Farrell, Tengyuan Liang, Sanjog MisraInterpretable ML
Department of Economics, UC Santa Barbara · Booth School of Business, University of Chicago

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xFODE: An Explainable Fuzzy Additive ODE Framework for System Identification

Ertugrul Kececi, Tufan Kumbasar

AI and Intelligent Systems Laboratory, Istanbul Technical University, 34469, Istanbul, Türkiye

Interpretable ML Nonlinear System Identification Fuzzy Logic System Identification

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Recent advances in Deep Learning (DL) have strengthened data-driven System Identification (SysID), with Neural and Fuzzy Ordinary Differential Equation (NODE/FODE) models achieving high accuracy in nonlinear dynamic modeling. Yet, system states in these frameworks are often reconstructed without clear physical meaning, and input contributions to the state derivatives remain difficult to interpret. To address these limitations, we propose Explainable FODE (xFODE), an interpretable SysID framework with integrated DL-based training. In xFODE, we define states in an incremental form to provide them with physical meanings. We employ fuzzy additive models to approximate the state derivative, thereby enhancing interpretability per input. To provide further interpretability, Partitioning Strategies (PSs) are developed, enabling the training of fuzzy additive models with explainability. By structuring the antecedent space during training so that only two consecutive rules are activated for any given input, PSs not only yield lower complexity for local inference but also enhance the interpretability of the antecedent space. To train xFODE, we present a DL framework with parameterized membership function learning that supports end-to-end optimization. Across benchmark SysID datasets, xFODE matches the accuracy of NODE, FODE, and NLARX models while providing interpretable insights.

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