Supervised Learning

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27 papers3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

MotherTree: Meta-learning on synthetic data improves decision tree training
Oct 7, 2026Ziyuan Wang, Fredrik D. JohanssonDecision Tree Learning Meta-Learning
Chalmers University of Technology University of Gothenburg
Integrated Imputation-Classification for Supervised Learning with Missing Data
Oct 3, 2026Yue Liu, Ben Liang, Ali Tizghadam +1Learning with Missing Data Supervised Learning
University of Toronto · TELUS
Real-Time Atomic-Resolution Electron Phase Imaging without Probe Calibration via Ptychography-Supervised Learning
Sep 22, 2026H. Yue, C. -C. Chen, C. -N. Hsiao +4Supervised Learning Electron Microscopy
College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai 200433, China. · Department of Engineering and System Science, National Tsing Hua University, Hsinchu 300044, Taiwan · National Center for Instrumentation Research, National Institutes of Applied Research, Hsinchu 300092, Taiwan +3
Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning
Sep 9, 2026Shaddin Dughmi, Alireza F. PourClassification Semi-Supervised Learning
University of Southern California · University of Waterloo
Algorithmic Principles For Multiclass Learning Are Hard To Come By: Limits of Regularization and Proper Learning
Aug 27, 2026Julian Asilis, Shaddin Dughmi, Vatsal Sharan +3Statistical Learning Theory Multiclass Classification
USC · University of Chicago · Northwestern University
Toward Machine Learning with the Unit as a Primitive: Learning from Unit-Linked Events
Aug 25, 2026Heyang GongSupervised Learning
Hide&Seek: Learning to Explain in an End-to-End Differentiable Network
Aug 17, 2026Tal Ellinson, Hadi Mohasel Afshar, Sally CrippsFeature Selection Interpretable ML
Human Technology Institute; School of Mathematical and Physical Sciences, University of Technology Sydney, Australia.
Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization
Jul 31, 2026Piotr Malkowski, Domenik Eichhorn, Joshua Ammermann +4Quantum Circuit Optimization Quantum Circuit Compilation
Karlsruhe Institute of Technology, Germany · Technical University of Munich, Germany
Rethinking Total Absorption Gamma Spectroscopy Deconvolution: Supervised Machine Learning vs Response-Matrix Methods
Jul 30, 2026J. Balibrea-Correa, E. N{á}cher, C. Fonseca-Vargas +1Spectral Methods Supervised Learning
Instituto de Física Corpuscular, CSIC - Universidad de Valencia, Spain, 46980, Valencia, Spain
Robust Regression of General ReLUs with Queries
Jun 9, 2026Ilias Diakonikolas, Daniel M. Kane, Mingchen MaReLU Neural Networks Active Learning
University of Wisconsin-Madison · University of California, San Diego
A Fiber Criterion for Representation Identifiability in Supervised Learning
May 31, 2026Vasileios SevetlidisRepresentation Identifiability Parameter Identifiability
Athena Research Center, Kimmeria Campus, Xanthi, Greece · Democritus University of Thrace, Vas. Sofias Campus, Xanthi, Greece · International Hellenic University, Serres, Greece
Universal Multiclass Transductive Online Learning
May 28, 2026Steve Hanneke, Hongao WangMulticlass Classification Transductive Learning
Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
Coupled Training with Privileged Information and Unlabeled Data
May 22, 2026Jiahao Shi, Omar Hagrass, Jason M. KlusowskiLearning Using Privileged Information Semi-Supervised Learning
Department of Electrical and Computer Engineering, Princeton University · Department of Operations Research and Financial Engineering, Princeton University
Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines
May 21, 2026David Mendez, Fernando Martin-Maroto, Gonzalo G. de PolaviejaClassification Image Classification
Mathematics of Behavior and Intelligence Lab, Champalimaud Foundation, Lisbon
The New Associationism: Lessons from Deep Learning
May 19, 2026Daniel RothschildCognitive Modeling Supervised Learning
Soft Learning
May 16, 2026Mohammed Aledhari, Ali Aledhari, Fatimah Aledhari +1Ensemble Learning Supervised Learning
Department of Data Science & Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA · Department of Computer and Information Science, Fordham University, New York, NY 10458, USA
Learning to Think from Multiple Thinkers
Apr 27, 2026Nirmit Joshi, Roey Magen, Nathan Srebro +2CoT Supervision Active Learning
Toyota Technological Institute at Chicago. · Weizmann Institute of Science. · New York University.
Learning to Query History: Nonstationary Classification via Learned Retrieval
Apr 8, 2026Jimmy Gammell, Bishal Thapaliya, Yoon Jung +3Classification Retrieval-Augmented Classification
Purdue University, Elmore Family School of Electrical and Computer Engineering. · Amazon. · Georgia Institute of Technology.
Stable but Wrong: When Learning Stabilizes Away from the Truth
Mar 23, 2026Zhipeng ZhangSupervised Learning
China Mobile Research Institute, Beijing 100053, China · China Mobile GBA (Greater Bay Area) Innovation Institute, Guangzhou 510656, China
Learning in Function Spaces: An Unified Functional Analytic View of Supervised and Unsupervised Learning
Mar 15, 2026K. LakshmananSupervised Learning Statistical Learning Theory
Department of Computer Science and Engineering, Indian Insititute of Technology (BHU), Varanasi 221005.
Relatively Smart: A New Approach for Instance-Optimal Learning
Mar 2, 2026Shaddin Dughmi, Alireza F. PourPAC Learning Semi-Supervised Learning
University of Southern California · University of Waterloo
Predicting Contextual Informativeness for Vocabulary Learning using Deep Learning
Feb 20, 2026Tao Wu, Adam KapelnerSupervised Learning In-Context Example Selection
Learning Without Training
Feb 20, 2026Ryan O'DowdFunction Approximation Supervised Learning
Claremont Graduate University
Machine Learning in Epidemiology
Feb 18, 2026Marvin N. Wright, Lukas Burk, Pegah Golchian +3Supervised Learning
Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany · University of Bremen, Germany · University of Copenhagen, Denmark
Machine learning Majorana topology using unsupervised and supervised learning
Dec 15, 2025Jacob Taylor, Haining Pan, Sankar Das SarmaUnsupervised Learning Supervised Learning
Condensed Matter Theory Center and Joint Quantum Institute, Department of Physics, University of Maryland, College Park, Maryland 20742, USA · Department of Physics and Astronomy, Center for Materials Theory, Rutgers University, Piscataway, NJ 08854, USA
Pairwise Difference Learning for Classification
Jun 28, 2024Mohamed Karim Belaid, Maximilian Rabus, Eyke HüllermeierClassification Meta-Learning
IDIADA Fahrzeugtechnik GmbH, Munich, Germany · Dr. Ing. h.c. F. Porsche AG Stuttgart, Germany · Ludwig-Maximilians-Universität Munich, Germany
How many labelers do you have? A closer look at gold-standard labels
Jun 24, 2022Chen Cheng, Hilal Asi, John DuchiModel Calibration Noisy-Label Learning
Department of Statistics, Stanford University · Department of Electrical Engineering, Stanford University · Departments of Statistics and Electrical Engineering, Stanford University

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MotherTree: Meta-learning on synthetic data improves decision tree training

Ziyuan Wang, Fredrik D. Johansson

Chalmers University of Technology University of Gothenburg

Decision Tree Learning Meta-Learning Supervised Learning Tabular Prediction Synthetic Data Pretraining

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Conventional decision tree algorithms produce effective, transparent models that can be audited, communicated, and deployed independently of the training data, but require learning every new task from scratch. In contrast, tabular foundation models demonstrate that meta-learning from a synthetic prior distribution enables strong in-context prediction for previously unseen tasks, especially in small-sample regimes. However, this approach does not produce a standalone model that can be inspected in isolation. We introduce MotherTree, a tabular transformer that meta-learns decision tree induction: given a training set for a new task, it outputs a hard, axis-aligned decision tree, equivalent in form to classically trained trees, in a single forward pass. MotherTree is pre-trained on a synthetic prior using stochastic gradient descent without requiring reference trees for supervision. On established benchmarks with controlled sample size, the approach is competitive with size-matched trees from common algorithms: recursive partitioning, gradient-based tree learning, globally optimal trees, and distillation from tabular foundation models. Notably, MotherTree consistently improves over from-scratch gradient-based learning and acts as a strong initializer: task-specific tuning of the generated tree outperforms the corresponding from-scratch learner on all benchmarks and sample sizes. These results show that meta-learning can provide effective inductive biases for learning stand-alone, small decision tree classifiers.