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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Temporally Interpretable Differentiable Decision Trees
Oct 7, 2026Eisuke Hirota, Aarav Sane, Rohan PalejaInterpretable ML Interpretability in RL
Department of Computer Science, Purdue University · Thomas Lord Department of Computer Science, University of Southern California
Revisiting Explainable AI through Model-Independent Concept Dictionaries
Oct 7, 2026Thomas Schnake, Doreen Schöppenthau, Alexander Meyer +3Explainable Artificial Intelligence Interpretable ML
Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Toronto, Canada · Vector Institute for Artificial Intelligence, Toronto, Canada · Institute for AI in Medicine (IKIM), Charité – Universitätsmedizin Berlin, Germany +5
What the Sleeve Feels: Explainable Machine Learning for Textile Pressure-Based Postural Screening
Oct 7, 2026Limon Bin Hossain, Md Sadib Rahman AnantaWearable Sensing Domain Generalization
Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1000, Bangladesh
X-OPM: Explainable Automatic Digital On-Chip Power Modeling for Enhanced Robustness
Oct 6, 2026Jingbo Jiang, Xizi Chen, Jian Peng +1Interpretable ML
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong · Huazhong Agricultural University, Wuhan, China
SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning
Oct 6, 2026Yikun Ou, Wei LiInterpretable ML Clinical Prediction
School of Computer Science, University of Sydney Sydney, Australia
Logic-Logit: A Logic-Based Approach to Choice Modeling
Oct 4, 2026Shuhan Zhang, Wendi Ren, Shuang LiHuman Preference Modeling Interpretable ML
CUHK-Shenzhen
Auditable Algebraic Counting Field for Cryptic-Pocket Detection from Apo Structures
Oct 1, 2026Shan Yu, Xuening WuInterpretable ML Structure-Based Drug Design
Pfizer, Shanghai, China
Factorized Scheduling Principle: Learning Interpretable and Transferable Policies via Structured Additive Functions
Sep 29, 2026Hong Je-Gal, Hyun-Suk LeeInterpretable ML Policy Optimization
Department of Artificial Intelligence and Robotics, Sejong University, South Korea · Artificial Intelligence Robotics Institute (AIRI), Sejong University, South Korea
Explainability from Training with Applications to TCR-Epitope Prediction
Sep 28, 2026Jiarui Li, Zixiang Yin, Samuel Landry +2Interpretable ML Neural Network Interpretability
Department of Computer Science, Tulane University · Department of Biochemistry and Molecular Biology, Tulane University School of Medicine
SR4-Fit: A Unified Interpretable Rule-Based Machine Learning Framework for Informative and Trustworthy Decision-Making
Sep 27, 2026Shyam Sundar Murali Krishnan, Dean Frederick HougenInterpretable ML
School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA
Linguistic Features for Interpretable Textual Entailment
Sep 21, 2026David Torres-Moreno, Jorge Hermosillo-Valadez, Asela Reig-AlamilloInterpretable ML Natural Language Inference
Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos · Centro Interdisciplinario de Investigación en Humanidades, Universidad Autónoma del Estado de Morelos
Beyond Point Prediction: Artificial Representative Trees with Uncertainty
Sep 21, 2026Lea L. Mairhöfer, Silke Szymczak, Björn-Hergen Laabs +1Interpretable ML Conformal Prediction
Institute of Medical Biometry and Statistics, University of Luebeck, Ratzeburger Allee 160 V24, 23562, Lübeck, Germany. · Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany. · CAIMed, Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine, Göttingen, Germany. +1
Falling Trees: A Model Class for Interpretable Risk Prioritization
Sep 20, 2026Varun Babbar, Zachery Boner, Margo Seltzer +1Interpretable ML Decision Tree Learning
Department of Computer Science, Duke University, Durham, NC, USA · Department of Computer Science, University of British Columbia, Vancouver, BC, CA.
Null importance: Disentangling relevance for interpretable machine learning
Sep 16, 2026Garvesh Raskutti, Kris Sankaran, Jiaxin YeFeature Attribution Interpretable ML
Department of Statistics, University of Wisconsin–Madison
Interpretable Multi-Instance Learning Enables Early Prediction of Key Molecular Alterations from Routine Flow Cytometry in Acute Myeloid Leukemia
Sep 16, 2026Jonathan Legrand, Aguirre Mimoun, Baudouin Denis de Senneville +3Clinical Decision Support Interpretable ML
IMB, MONC · Univ. Bordeaux, CNRS, Inria, Bordeaux INP, IMB, UMR 5251, F-33400 Talence, France · CHU Bordeaux +5
A unified framework for global and local interpretability using adaptive derivative-ordered random explanation
Sep 15, 2026Lemen Chao, Ming Lei, Anran FangGradient-Based Attribution Feature Attribution
School of Information Resource Management, Renmin University of China, Beijing, 100872, People’s Republic of China. · Key Laboratory of Data Engineering and Knowledge Engineering, Beijing, 100872, People’s Republic of China.
Can We Do Interpretable NLI with Graphs Based on Atomic Propositions?
Sep 15, 2026Younes Boufouss, Luc Pommeret, Thomas Gerald +2Interpretable ML Natural Language Inference
STL, LISN · Université Paris-Saclay, CNRS, LISN, Orsay, France · STL, CNRS, LISN +2
Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details
Sep 14, 2026Lemen Chao, Zixuan Yang, Anran Fang +2Interpretable ML Explainability Evaluation
a Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Renmin University of China · b School of Information Resource Management, Renmin University of China
The Misery of Mechanistic Interpretability: A Formal Perspective
Sep 14, 2026Tobias Ladner, Matthias AlthoffNeural Network Verification LLM Interpretability
Technical University of Munich, Germany
Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration
Sep 14, 2026Yuning Wang, Iman Azimi, Amir M. Rahmani +1Concept Bottleneck Models Interpretable ML
University of Turku, Department of Computing, Turku, Finland · University of California, Irvine, Department of Computer Science, California, USA
SeqMaestro: From nucleotide sequences to biological hypotheses through interpretable machine learning
Sep 14, 2026Evgeny S. Saveliev, Krzysztof Kacprzyk, Charlotte Capitanchik +7Interpretable ML Genomics
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK · The Francis Crick Institute, London, UK · UK Dementia Research Institute at King’s College London, London, UK +1
A Multi-Stage Rule-Chaining Framework for Compositional and Interpretable Cognitive Reasoning
Sep 12, 2026Deblina KarCompositional Reasoning Interpretable ML
Indian Institute of Technology Kharagpur
Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models
Sep 11, 2026Raad Bin Tareaf, Murad Al-Rajab, Samia Loucif +2Data Leakage Tabular Foundation Models
Data Science and AI Cluster, XU Exponential University of Applied Sciences, Potsdam, Germany · German University of Digital Science, Potsdam, Germany · College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates +2
Translation of Black-Box Clinical Prediction Models into Standalone Transparent Nomograms: Temporal External Validation in Heart Transplantation
Sep 7, 2026Henry Pigot, Paulo J. G. Lisboa, Sandra Ortega-Martorell +3Surrogate Modeling Healthcare
Department of Translational Medicine, Artificial Intelligence and Bioinformatics in Cardiothoracic Sciences Research Unit, Lund University, Lund, Sweden · Artificial Intelligence and Digital Technologies Research Institute, Liverpool John Moores University, Liverpool, UK · Department of Thoracic and Vascular Surgery, Skåne University Hospital, Lund, Sweden
SMILE: Bridging Continuous Optimization and Discrete Symbolic Recovery
Sep 4, 2026Mansooreh Montazerin, Antonio Ortega, Ajitesh SrivastavaInterpretable ML Symbolic Regression
Department of Electrical and Computer Engineering University of Southern California
Translation as a Decision Space: A Multi-Agent Perspective on Low-Resource Dialect Generation
Sep 3, 2026Hasan Alkhder, Mohammad Abboush, Igor Tchappi +2Interpretable ML Low-Resource MT
MURANO: Design, Run, and Reproduce Mechanistic Interpretability Experiments as Composable Pipelines
Aug 31, 2026Alireza Bayat Makou, Emirhan Böge, Phu Gia Hoang +5LLM Interpretability Interpretable ML
Ubiquitous Knowledge Processing (UKP) Lab, Technical University of Darmstadt, Germany · Cluster of Excellence “Reasonable Artificial Intelligence” (RAI), hessian.AI, Germany · National Research Center for Applied Cybersecurity ATHENE, Germany +1
FaVOR: LLM-Based Agentic Framework for Factor Mining via Empirical Validation
Aug 31, 2026Hyeonjin Kim, Minseok Kim, Seunghyeon Jung +3Quantitative Finance Interpretable ML
Department of Computer Science and Artificial Intelligence, Dongguk University-Seoul · Department of Industrial Engineering, Seoul National University · School of Finance, Soongsil University
INTERVenE: Temporal-Abstraction-Interval Based Transformers for Short-Horizon Medical Event Prediction
Aug 30, 2026Shahar Oded, Yuval ShaharEvent Time Prediction Interpretable ML
The Stein Faculty of Computer and Information Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel
The Latent Diagnostic Taxonomy: A Framework for Constructing Classifiers and Diagnosing Their Decisions, Applied to Prompt Injection Detection
Aug 26, 2026Jaturong Kongmanee, Smile ThanapattheerakulInterpretable ML Prompt Injection Defense
Research Incubation, TrendAI™Research

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Temporally Interpretable Differentiable Decision Trees

Eisuke Hirota, Aarav Sane, Rohan Paleja

Department of Computer Science, Purdue University · Thomas Lord Department of Computer Science, University of Southern California

Interpretable ML Interpretability in RL Reinforcement Learning Policy Gradient Methods

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Interpretability offers a solution to safe autonomy by providing transparency into an agent's underlying decision-making model. Within sequential-decision making tasks, differentiable decision trees (DDTs) are one approach to such interpretability, maintaining automatic-differentiable policies while providing humans with a discrete tree-based visualization. Nonetheless, current implementations of DDTs are not well-suited for sequential-decision making domains, as there exists an inherent mismatch between a tree's single-timestep behavior and a human's multi-timestep planning. Our work thus introduces time as a new dimension of interpretability, coined as temporal interpretability, and demonstrates how temporal abstractions via action chunking improve it. We achieve this by first introducing two novel policy gradient algorithms that incorporate action chunking. Additionally, to maintain parameter-efficient trees, we develop an information-theoretic tree restructuring algorithm that modifies the tree during training. Across four simulation environments, we find that warm-starting action chunked DDTs from a distilled action chunked policy is the most effective way to obtain temporally interpretable trees: they match neural network policies in three of the four domains while using up to 80

\%

fewer parameters. Our code is available at https://github.com/ei5uke/temp-interp.

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