Decision Tree Learning

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7 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification
Oct 7, 2026Yue Qiu, Zekang Du, Yiqun Diao +2Tabular Classification Decision Tree Learning
School of Computer Science and Technology, Huazhong University of Science and Technology · School of Computing, National University of Singapore
Discovery of fully efficient fault indicators along a data-based diagnosis process
Sep 23, 2026Igor Bezmaternykh, Louise Travé-Massuyès, Elodie ChantheryDecision Tree Learning Fault Diagnosis
INSA Toulouse · LAAS-CNRS, Université de Toulouse, INSA, Toulouse, France · LAAS-DISCO, Comue de Toulouse, ANITI +1
An Exact Junction-Tree Extended Formulation for Optimal Classification Trees
Sep 21, 2026Jiancheng TU, WenqiFanDecision Tree Learning Classification
Department of Computing, The Hong Kong Polytechnic University · Department of Management and Marketing, The Hong Kong Polytechnic University
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
Explainable Predictive Condition-based Maintenance of Naval-Propulsion Systems using Fuzzy Logic
Sep 21, 2026Dionisis Kalogeropoulos, Georgia Sovatzidi, Panagiotis G. Kalozoumis +1Predictive Maintenance Decision Tree Learning
Department of Computer Science and Biomedical Informatics, University of Thessaly
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.
Learned Look-Ahead Splitting Rule for CART
Sep 14, 2026Andrew Gao, Tianlin Liu, Ruichen Han +1Decision Tree Learning Classification
Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA · Lynbrook High School, San Jose, CA 95129, USA · Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
Literati: Towards Anytime Optimal Shape Generalized Trees via AO*
Sep 8, 2026Nakul Upadhya, Eldan CohenTree Search Decision Tree Learning
Department of Mechanical and Industrial Engineering University of Toronto, Toronto, Canada
Learning Sparse Decision Trees via Transformer Variational Auto-Encoders
Sep 1, 2026Giacomo Fidone, Alessio Cascione, Riccardo GuidottiVariational Autoencoders Structured Sparsity
University of Pisa · ISTI-CNR
GeoAI-based post-segmentation quality validation of building footprints via spatial feature engineering
Aug 10, 2026Shah Imran Ahsan Chowdhury, Kazi Jihadur Rashid, Rajsree Das Tuli +2Building Footprint Extraction Decision Tree Learning
Department of Computer Science and Engineering Jahangirnagar University Dhaka 1342, Bangladesh · Department of Geography Florida State University Tallahassee, FL 32306, USA · Department of Geography and Environmental Sustainability The University of Oklahoma Norman, OK 73019, USA +1
Backward Compatibility in Tree-Based Explanations and Enhanced CART Algorithm
Aug 9, 2026Hirofumi SuzukiExplanation Stability Decision Tree Learning
Fujitsu Limited Kawasaki, Kanagawa, Japan
Handling Missing Data in Probabilistic Regression Trees
Aug 6, 2026Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme PumiLearning with Missing Data Decision Tree Learning
a Graduate Program in Statistics - Universidade Federal do Rio Grande do Sul.
ArborEnum: Decision Tree Rashomon Sets over Continuous Features
Aug 5, 2026Zakk Heile, Hayden McTavish, Margo Seltzer +1Decision Tree Learning
Department of Computer Science, Duke University, Durham, USA · Department of Computer Science, University of British Columbia, Vancouver, Canada
Search Strategies for Optimal Classification and Regression Trees
Jul 30, 2026Jacobus G. M. van der Linden, Mim van den Bos, Emir DemirovićDecision Tree Learning Classification
Delft University of Technology
dtControl2+ $\varepsilon$ : Trading Optimality for Explainability in MDPs via Decision Trees
Jul 28, 2026Tereza Kinská, Jan Křetínský, Tobias Meggendorfer +2Markov Decision Processes Markov Models
Masaryk University, Brno, Czech Republic · Technical University of Munich, Munich, Germany · Lancaster University Leipzig, Leipzig, Germany +1
Mind the Missing Split: Resolving Feature Heterogeneity in Swarm Learning with Random Forests
Jul 28, 2026Mohammad Tajabadi, Dominik HeiderLearning with Missing Data Decentralized Learning
Institute of Medical Informatics, University of Münster, Münster, Germany
Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data
Jul 15, 2026Youssef Drissi, Markus Ettl, Shivaram Subramanian +2Decision Tree Learning Dynamic Pricing
IBM Research
Relevance-Aware Rule: Structural Deletion of Irrelevant Conditions in Decision Trees
Jul 15, 2026Jung-Sik Hong, Jeongeon Lee, Min Kyu Sim +1Decision Tree Learning
Department of Data Science, Seoul National University of Science and Technology
Algebraic Model Counting for Global Analysis of Optimal Decision Trees
Jul 2, 2026Hiroki ArimuraDecision Tree Learning
Hokkaido University, N14 W9, Kita-ku, Sapporo, Hokkaido, 060-0814 Japan
Conditional Inference Trees and Forests for Feature Selection
Jul 1, 2026Robert Milletich, Justin Downes, Steve Goley +1Decision Tree Learning Feature Selection
Amazon Web Services
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
Surrogate-Assisted Framework for SI-Compliant Interconnect Design Optimization Using the Earth Mover's Distance
Jun 13, 2026Emre Ecik, Werner John, Julian Withöft +2Surrogate-Assisted Optimization Electronic Design Automation
Information Processing Lab, TU Dortmund University, 44227 Dortmund, Germany · Pyramide2525/TU Dortmund University, 33100 Paderborn, Germany · EMC Technology Center Paderborn, Zuken GmbH, 33100 Paderborn, Germany
A Bregman Perspective on Classification and Regression Trees
Jun 12, 2026Mathias BourelFunctional Bregman Divergences Decision Tree Learning
IESTA, Facultad de Ciencias Económicas y de Administración, Universidad de la República, Uruguay · IRL-2030, Instituto Franco-Uruguayo de Matemática e Interacciones (IFUMI)
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.
Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors
May 31, 2026Livija Jakaite, Vitaly SchetininSample Complexity Bayesian Inference
School of Computing and Engineering University of Bedfordshire, Luton, UK
Interpretable Policy Distillation for Power Grid Topology Control
May 30, 2026Aleksandra Dmitruka, Karlis FreivaldsRL Control Reinforcement Learning
University of Latvia, Faculty of Exact Sciences and Technology, Riga, Latvia
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.
Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference
May 29, 2026Salim I. Amoukou, Saumitra Mishra, Manuela VelosoAnytime-Valid Inference Decision Tree Learning
J.P. Morgan AI Research.
Forecasting Japanese elections: A nonlinear machine-learning approach
May 26, 2026Sota Kato, Xuan Luo, Budrul Ahsan +2Decision Tree Learning
International University of Japan · The Tokyo Foundation · IBM Japan +2
Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models
May 22, 2026Hongyi Li, Jun Xu, Hong YanGradient-Boosted Decision Trees Decision Tree Learning
School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, 518055, China and with the Shenzhen Key Lab for Advanced Motion Control and Modern Automation Equipments, Shenzhen, 518055, China · Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification

Yue Qiu, Zekang Du, Yiqun Diao, Bingsheng He+1

School of Computer Science and Technology, Huazhong University of Science and Technology · School of Computing, National University of Singapore

Tabular Classification Decision Tree Learning LLM Prompting Knowledge Distillation

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While Large Language Models (LLMs) possess rich world knowledge and impressive generalization capabilities, their direct application to tabular data classification is hindered by high inference costs and limited interpretability. In contrast, decision trees are fast and transparent but often underperform in low-data regimes. In this work, we propose a novel framework that bridges these paradigms by distilling LLM knowledge into interpretable decision trees under a few-shot learning setting. Instead of directly prompting the LLM to generate full trees, which is often unstable and inefficient, we develop a three-stage paradigm that prompts the LLM to generate rules and organize the rules into a tree. Experiments on multiple real-world tabular datasets demonstrate that our method achieves superior accuracy and interpretability with significantly lower prompting overhead compared to existing baselines.

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