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

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All you need is SAMPAT
Jul 10, 2026Jayadeva, Madhur AswaniShallow Neural Networks Interpretable ML
Department of Electrical Engineering, Indian Institute of Technology, Delhi
Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
Jul 9, 2026Yann Claes, Pierre Geurts, Vân Anh Huynh-ThuInterpretable ML Neural Network Interpretability
Montefiore Institute University of Liège
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery
Jul 9, 2026Fuling Chen, Kevin Vinsen, Phillip Melton +1Interpretable ML Scientific ML
International Centre for Radio Astronomy Research (ICRAR), University of Western Australia, Perth, WA, Australia · The Raine Study, University of Western Australia, Perth, WA, Australia · Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia +1
Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives
Jul 8, 2026Thibaut Vidal, Julien FerryInterpretable ML Certified Robustness
CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
Jul 8, 2026Ignacio D. Lopez-Miguel, Ezio Bartocci, Thomas Eiter +1Reinforcement Learning Interpretable ML
TU Wien Vienna, Austria
Complexity-Budgeted, Interaction-Aware Interpretable Model for Tabular Data
Jul 8, 2026Srikumar KrishnamoorthyFeature Interaction Modeling Interpretable ML
Information Systems Area, Indian Institute of Management Ahmedabad, India
Efficient Bayesian Deep Ensembles via Analytic Predictive Inference
Jul 7, 2026Sina Aghaee Dabaghan Fard, Marie Maros, Jaesung LeeUncertainty Quantification Interpretable ML
Wm Michael Barnes ’64 Department of Industrial and Systems Engineering Texas A&M University College Station, TX, USA
ExplAIner: A Declarative Query Language for Explaining Classification Models
Jul 7, 2026Marcelo Arenas, Pablo Barceló, Diego Bustamante +3Explainable Artificial Intelligence Interpretable ML
Pontificia Universidad Católica de Chile, Chile · Carnegie Mellon University, USA
Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
Jul 7, 2026Franz Motzkus, Sebastian BernhardEnd-to-End Autonomous Driving Interpretable ML
AUMOVIO, Germany · University of Bamberg, Germany
Platonic Projection Structures: Operator-Induced Observability in Representation Learning
Jul 6, 2026Kazuo Ishii, Bishnu Prasad Gautam, Jieling Wu +1Representation Geometry Representation Learning
Department of Applied Information Engineering, Faculty of Engineering, Suwa University of Science, Chino 391-0292, Nagano, Japan · Department of Information Engineering, Kanazawa Gakuin University, Kanazawa 920-1392, Ishikawa, Japan
Caption Bottleneck Models
Jul 1, 2026Seref Baris Cagliyan, Umut Ozdemir, Merve Tapli +1Vision-Language Models Concept Bottleneck Models
Dept. of Computer Eng., Middle East Technical University (METU) · Robotics & AI Center (ROMER), METU
Multilayer Q-Matrix-Embedded Neural Network for Cognitive Diagnosis (M-QCDNet): Structure-Aware Deep Learning Architecture for Psychometric Interpretability
Jul 1, 2026Yiyao YangEducational Assessment Interpretable ML
Columbia University, New York, NY, United States
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Jun 30, 2026Shiyi Chen, Nicholas Saban, Collin Hargreaves +1Interpretable ML Remote Sensing
University of California, Berkeley, California, United States.
Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again
Jun 30, 2026Zakk Heile, Hayden McTavish, Margo Seltzer +1Ensemble Learning Learning to Defer
Department of Computer Science · Duke University · Durham, USA +2
Structure-Regularized Interpretable TCR-Epitope Prediction
Jun 29, 2026Jiarui Li, Zixiang Yin, Yunbei Zhang +4Explainable Artificial Intelligence Interpretable ML
Department of Computer Science, Tulane University · Department of Biochemistry and Molecular Biology, Tulane University School of Medicine
Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization
Jun 29, 2026Hugo L. Hammer, Vajira Thambawita, Kristoffer Herland Hellton +1Interpretable ML Neural Network Interpretability
1Oslo Metropolitan University (OsloMet) · 2Simula Metropolitan Center for Digital Engineering (SimulaMet)
Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models
Jun 28, 2026Nick Oh, Helen JinInterpretable ML Scientific ML
socius labs, London, United Kingdom · Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning
Jun 25, 2026Stanisław Sójka, Felix Steffek, Matthias GrabmairLegal NLP AI-Assisted Decision Making
School of Computation, Information, and Technology, Technical University of Munich, Germany · Faculty of Law, University of Cambridge
Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
Jun 24, 2026Rikab Gambhir, Luisa Lucie-Smith, Jesse ThalerExplainable Artificial Intelligence Interpretable ML
Department of Physics, University of Cincinnati, Cincinnati, Ohio 45221, USA · Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, 21029 Hamburg,2026 Germany · Center for Theoretical Physics – a Leinweber Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USAJun +3
Interpretable Concept-Guided Polynomial Tabular Kolmogorov-Arnold Network for EEG-Based Mild Cognitive Impairment Detection
Jun 24, 2026Yosef Bernardus Wirian, Qiang ChengElectroencephalography Interpretable ML
Computer Science Department, University of Kentucky, 351 Ralph G. Anderson Building, Lexington, 40506, KY, USA · Institute for Biomedical Informatics, University of Kentucky, 760 Press Ave, 40508, Lexington, KY, USA
What Does a Pathological Speech Assessment Model Know about Acoustic Features? A Case Study on Oral and Oropharyngeal Cancer Patients
Jun 23, 2026Tuan Nguyen, Corinne Fredouille, Alain Ghio +2Speech Quality Assessment Interpretable ML
LIA, AU · Avignon University, LIA, UPR 4128, Avignon, France · AU, LIA +5
Discovering Latent Groups for Robust Classification
Jun 22, 2026Ankur Garg, Ulrich Aïvodji, Samira Ebrahimi Kahou +1Interpretable ML Neural Network Robustness
Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning
Jun 22, 2026Jing-Zong Zhang, Wang-Mei Zha, Lingxiao Wang +1Interpretable ML High-Energy Physics
Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China · Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China · RIKEN Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), Wako, Saitama 351-0198, Japan +2
Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability
Jun 18, 2026Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4Interpretable ML Text Classification
Stanford University · Independent Researcher · University of California, Irvine +1
Can Physician Expertise Improve Machine Learning Identification of Delirium?
Jun 16, 2026Xinyu Qin, Vicky Ye, Ruiheng Yu +1Clinical Decision Support Interpretable ML
Department of Biomedical Engineering, University of Houston, USA · Paul G. Allen School of Computer Science & Engineering, University of Washington, USA · Department of Health Systems & Population Health Sciences, University of Houston, USA
Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules
Jun 15, 2026Wei Xu, Ke Yang, Gang Luo +4Clinical Decision Support Interpretable ML
The Centre for Artificial Intelligence Driven Drug Discovery, Macao Polytechnic University, Macao SAR · The Key Laboratory of Short-Range Radio Equipment Testing and Evaluation, Ministry of Industry and Information Technology, and the Terahertz Science Application Center (TSAC), Beijing Institute of Technology, Zhuhai, China · The Faculty of Education, The University of Hong Kong, Hong Kong SAR +2
The limits of interpretability in multiple linear regression
Jun 14, 2026Anand Sharma, Chen Liu, Daniele Coslovich +1Interpretable ML Linear Regression
Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pashan, Pune 411008, India · Innovation and Research Division, Ge-Room Inc., 93160 Noisy le Grand, France · Dipartimento di Fisica, Universit`a di Trieste, Strada Costiera 11, 34151, Trieste, Italy∗ +1
Theorem-Grounded Execution Ontologies for Interpretable Machine Reasoning
Jun 14, 2026Raghu AnantharangacharInterpretable ML Formal Verification
Independent Researcher · Bangalore, India
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
Jun 12, 2026Farica Zhuang, Seong Woo Han, Zixuan Wen +3Mixture of Experts Survival Analysis
University of Pennsylvania · Yale University
Machine Learning for Biomedical Raman Spectroscopy: From Spectral Acquisition to Clinical Translation
Jun 12, 2026Bogdan Oancea, Ana Maria Seciu-Grama, Nicoleta Siminea +10Interpretable ML Raman Spectroscopy
National Institute of Research and Development for Biological Sciences, Romania · University of Bucharest, Romania · University of Turku, Finland +1

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All you need is SAMPAT

Jayadeva, Madhur Aswani

Department of Electrical Engineering, Indian Institute of Technology, Delhi

Shallow Neural Networks Interpretable ML Neural Network Interpretability Neural Network Approximation Theory Neural Network Expressivity

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The current state of the art in AI/ML rests on deep neural architectures, which, in general, suffer from a lack of interpretability. Interpretability is crucial to gleaning insights while analyzing experimental data, where quantitative predictions may not be adequate for a scientist. We present a three layer neural architecture, SAMPAT (Smooth Approximation via Multivariate Polynomials and Analytic Transformations), that can provably learn a continuous, everywhere differentiable function, that can approximate any smooth function arbitrarily closely. SAMPAT's approximant can be expressed as a closed and compact algebraic, analytic expression, providing complete interpretability. Experiments on synthetic and benchmark datasets indicate that SAMPAT yields competitive performance with simpler representations. For many tasks, a two layer SAMPAT suffices. By imposing restrictions on the connectivity between neurons, SAMPAT may be used to provide a range of approximants, including regular and trigonometric polynomials, rational expressions, Gaussians, mixtures of Gaussians, as well as arbitrary combinations of the same; without restrictions, it learns a suitable structure. SAMPAT may be used to factorize polynomials and model nonlinear systems. With the addition of skip connections, a 4 to 6 layer SAMPAT is adequate to represent a substantive range of methods widely used in AI/ML, allowing the choice of a model's family, not just its parameters, to also be optimized as part of the learning process.

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