Interpretable ML

ML: Machine Learning

Follow this topic Ask about this topic

Momentum

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

Latest papers 206

All topics

Learning High Coverage Discriminative Parsimonious Rulesets
Jun 12, 2026Mariamma Antony, Raman Sankaran, Chiranjib Bhattacharyya +1Interpretable ML
Indian Institute of Science, CV Raman Rd, Bengaluru, Karnataka 560012 · Compass, Bengaluru, Karnataka 560012
Simultaneous Latent Budget Trees for Stratified Classification
Jun 11, 2026Cristian Buoncompagni, Stefano Pellegrino, Giulia Vannucci +2Latent Variable Models Interpretable ML
Department of Physics, University of Naples Federico II, via Cintia 21, Napoli, 80125, Italy. · Department of Electrical Engineering and Information Technologies, University of Naples Federico II, via Claudio 21, Napoli, 80125, Italy. · Department of Neuroscience, Reproductive Sciences and Odontostomatology, University of Naples Federico II, via Sergio Pansini 5, Napoli, 80131, Italy.
Information Lattice Learning as Probabilistic Graphical Model Structure Learning
Jun 11, 2026Haizi Yu, Lav R. VarshneyGraph Structure Learning Interpretable ML
Kocree, Inc. · AI Innovation Institute, Stony Brook University
Interpretable Factor Decomposition for Decision Intelligence in Large-Scale Financial Markets: Evidence from China's A-Share Market
Jun 11, 2026Xiao Han, Yao Xiao, Zhen Zhang +1Quantitative Finance Interpretable ML
Goizueta Business School Emory University Atlanta, GA, USA · College of Computing Georgia Institute of Technology Atlanta, GA, USA · School of Data Science University of Pennsylvania Philadelphia, PA, USA +1
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Jun 11, 2026Yixiao Wang, Hayden McTavish, Varun Babbar +2Tree Search Interpretable ML
Department of Computer Science, Duke University, Durham, USA · Department of Computer Science, University of British Columbia, Vancouver, Canada
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics
Jun 10, 2026Pietro Barbiero, Giovanni De Felice, Mateo Espinosa Zarlenga +5Interpretable ML
IBM Research (CH) · Universit`a della Svizzera Italiana (CH) · University of Oxford (UK) +1
3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling
Jun 9, 2026Ahmad Al-KabbanyConcept Bottleneck Models Interpretable ML
Yubree Labs, Ottawa K2M1T2, Canada · Multimedia Interaction and Communication Lab, Arab Academy for Science and Technology, Alexandria 21937, Egypt
In Defense of Information Leakage in Concept-based Models
Jun 9, 2026Mateo Espinosa ZarlengaConcept Bottleneck Models Interpretable ML
University of Oxford, UK.
Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice
Jun 9, 2026Kazuki Kawamura, Fujiki Nakamura, Hayato Nishioka +3Interpretable ML Music Information Retrieval
The University of Tokyo · Sony Computer Science Laboratories
SSL-GMMVC: Interpretable Voice Conversion via Locally Linear GMM Transforms in Self-Supervised Representation Space
Jun 9, 2026Tomoya Tanabu, Hiroshi Nishijima, Daisuke Saito +1Gaussian Mixture Models Interpretable ML
The University of Tokyo, Japan
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
Jun 7, 2026Varun Khurana, Vijval Ekbote, Vashu Chauhan +3Interpretable ML Automated Feature Engineering
Adobe Media and Data Science Research · IIIT-Delhi
One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability
Jun 7, 2026Bhavith Chandra Challagundla, Sanskar Pandey, Param Thakkar +7World Models Interpretable ML
New York University · Independent Researcher · Veermata Jijabai Technological Institute +5
Biological Reasoning-Informed Regression for Interpretable Regulatory DNA Activity Prediction
Jun 6, 2026Yi Duan, Zhao Yang, Jiwei Zhu +3Interpretable ML
Gaoling School of Artificial Intelligence · Renmin University of China · Beijing, China +1
Enhancing AI Interpretability and Safety through Localised Architectures
Jun 6, 2026Ian Seet, Jonas Bozenhard, Simon OstermannEnergy-Efficient ML Interpretable ML
University of Glasgow · Hamburg University of Technology · Saarland University +2
Explaining Unsupervised Disease Staging in Huntington's Disease: Insights into Model Representations and Clusters
Jun 5, 2026Lubna Mahmoud Abu Zohair, Hind ZantoutInterpretable ML Unsupervised Clustering
Heriot-Watt University, Dubai, United Arab Emirates
Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia
Jun 4, 2026Jonathan Colen, Eric Werner, Maryam Golbazi +6Healthcare Interpretable ML
Joint Institute on Advanced Computing for Energy and Science, Old Dominion University, Suffolk, VA, 23535, U.S.A. · School of Data Science, Old Dominion University, Norfolk, VA, 23529, U.S.A. · Eastern Virginia Medical School, Macon & Joan Brock Virginia Health Sciences, Old Dominion University, Norfolk, VA, 23501, U.S.A. +5
Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
Jun 3, 2026Deepika SN Vemuri, Sayanta Adhikari, Ankit Saha +2Visual Representation Learning Interpretable ML
IIT Hyderabad · Amazon, India · KLA +1
From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets
May 29, 2026Zakk Heile, Hayden McTavish, Varun Babbar +2Interpretable ML Decision Tree Learning
Department of Computer Science, Duke University, Durham, USA · Department of Computer Science, University of British Columbia, Vancouver, Canada.
Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods
May 29, 2026Shasha Zhou, Mingyu Huang, Ke LiInterpretable ML Genomics
Department of Computer Science, University of Exeter, Exeter, UK.
Beyond Additive Decompositions: Interpretability Through Separability
May 29, 2026Jinyang Liu, Munir Eberhardt HiabuInterpretable ML Statistical Learning Theory
Department of Mathematical Sciences, University of Copenhagen, Denmark.
FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Prediction
May 29, 2026Zijie Zhao, Roy E. WelschInterpretable ML Corruption Robustness
EECS Department, Massachusetts Institute of Technology, Cambridge, MA, USA · Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
QDSP: An Interpretable Structured Learning Framework for Predicting Death or Cerebral Palsy in Very Low Birth Weight Infants
May 29, 2026Ling Wang, Xiaolong Li, Hui Zhou +4Interpretable ML Clinical Outcome Prediction
College of Computer Science Sichuan Normal University Chengdu, Sichuan, China · West China Second University Hospital Sichuan University Chengdu, Sichuan, China
STEP: Learning STructured Embeddings for Progressive Time Series
May 29, 2026Lucas Thil, Jesse Read, Rim Kaddah +1Self-Supervised Time Series Representation Learning Interpretable ML
LIX, École Polytechnique IRT SystemX Palaiseau, France · IRT SystemX Palaiseau, France · Safran Tech Chateaufort, France
DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks
May 29, 2026Jyotirmoy Singh, Anushka Roy, Shreea Bose +1Interpretable ML Interpretable Anomaly Detection
Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis
May 28, 2026Thalea Schlender, Peter A. N. Bosman, Tanja AlderliestenMulti-Objective Evolutionary Optimization Survival Analysis
Leiden University Medical Center, Leiden, The Netherlands · Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
CB-SLICE: Concept-Based Interpretable Error Slice Discovery
May 28, 2026Yael Konforti, Mateo Espinosa Zarlenga, Elaf Almahmoud +1Interpretable ML
Department of Computer Science and Technology, University of Cambridge, Cambridge, UK · Trinity College, University of Oxford, Oxford, UK · Cambridge Institute for Technology and Humanity, Cambridge, UK
Evolutionary Rule Extraction from Corporate Default Prediction Models
May 28, 2026Desirè Fabbretti, Matteo Pasquino, Elia Pacioni +2Interpretable ML Credit Scoring
Department of Management, Università Politecnica delle Marche, Ancona, Italy · HES-SO Valais-Wallis, 3960 Sierre, Switzerland · University of Extremadura, 06800 Mérida, Spain
When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models
May 27, 2026Ziba Jabbar Zare, Ulrich Aïvodji, Julien Ferry +1Interpretable ML Algorithmic Fairness
Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Canada · Software and Information Technology Engineering Department, École de technologie supérieure, Canada · CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Canada
Courtroom Analogy: New Perspective on Uncertainty-Aware Classification
May 25, 2026Taeseong Yoon, Heeyoung KimUncertainty Quantification Interpretable ML
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
ConceptM $^3$ oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology
May 23, 2026Xuan Wang, Zhongling Xu, Gopi Kannedhara +13Computational Pathology Concept Bottleneck Models
University of Texas at Austin, Austin, TX, USA · University of North Carolina at Chapel Hill, NC, USA · Dell Children’s Medical Center, Austin, TX, USA +2

Jun 12, 2026, cs.LGJ/K move · Enter open · S save

Learning High Coverage Discriminative Parsimonious Rulesets

Mariamma Antony, Raman Sankaran, Chiranjib Bhattacharyya, Uma Satya Ranjan

Indian Institute of Science, CV Raman Rd, Bengaluru, Karnataka 560012 · Compass, Bengaluru, Karnataka 560012

Interpretable ML

Open paper AskPDF arXiv

Learning systems based on IF-THEN rule representations readily offer interpretability, making them a crucial focus in contemporary AI research. A key objective for such rule sets is to achieve both high discriminative power and interpretability. While existing state-of-the-art algorithms implicitly prioritize predictive accuracy, they often fall short on one or more quality metrics that ensure interpretability, such as coverage and parsimony of rule sets. Motivated by this, this paper propose the development of CDPR, which aims to create highly accurate and interpretable rule sets for classification problems. To the best of our knowledge, this represents the first attempt to establish such an approach. In this study, we introduce two algorithms rooted in submodular maximization, which not only provide provable guarantees on coverage but also yield rule sets that are both discriminative and parsimonious. We empirically demonstrate that rule sets learned through our approaches achieve higher accuracy and interpretability and has more than a 2.5-fold improvement in average coverage rates when compared to the next best algorithm.

Momentum

Related topics

Latest papers 206