Ensemble Learning

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Overview

Ensemble learning studies how to combine predictions from multiple models, from Random Forests and boosting to stacking, voting, and mixtures of experts. Methods may average outputs, learn weights from validation data, or select and route among specialists for each example, with applications ranging from tabular prediction and medical imaging to weather forecasting and language models. Combining models can reduce errors and quantify uncertainty, but gains depend on whether their mistakes are complementary rather than correlated. Current work also weighs accuracy against calibration, computational cost, distribution shifts, and leakage-safe evaluation, while developing theoretical guarantees for aggregation and boosting.

Latest papers

CardsList

278 papers25 papers in the last four weeks, down 24% on the four weeks before. 0.1% of all new papers.

  1. Rethinking Neural Thickets Without Weight Perturbations

    Oct 29, 2026Nicolas Dufour, Elouan Gardès, Amil Dravid +2LLM ReasoningEnsemble Learning

    Kyutai · Meta FAIR · LIGM, CNRS, Univ Gustave Eiffel, ENPC, IP Paris +2

  2. CBE: A Complexity-adaptive Boundary-aware Ensemble Method for Imbalanced Data

    Oct 29, 2026Xun WeiClass-Imbalanced LearningClassification

    School of Industrial Software, Jiangxi University of Science and Technology, Nanchang 330044, China

  3. ReForest: Interpretability Meets Reasoning in Isolation Forest

    Oct 27, 2026Juyoung Seo, Geonwoo Bang, Dongho Kim +1Anomaly DetectionInterpretable Anomaly Detection

    Department of Computer Education, Sungkyunkwan University, Seoul, Korea · Department of Immersive Media Engineering, Sungkyunkwan University, Seoul, Korea · Department of Computer Education/Social Innovation Convergence Program, Sungkyunkwan University, Seoul, Korea

  4. Selective Transfer Learning for Groundwater Level Anomaly Forecasting: Accuracy-Risk Trade-offs

    Oct 27, 2026Yaxuan Wu, Ali Mohammed Mansoor Alsahag, Seyed Sahand Mohammadi ZiabariTransfer LearningCross-Domain Transfer Learning

    Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands

  5. Multibranch Ensemble Head in Vision Foundation Models for Efficient Cucumber Phenological Stage Classification

    Oct 27, 2026Kostiantyn KhabarlakVision Foundation Model AdaptationEnsemble Learning

    Dnipro University of Technology, Ukraine

  6. Recall or Relearn? A Mixture of In-Context Experts for Recurring Concept Drift

    Oct 27, 2026Zhangkai Wu, Xu Zhang, Zhihong Cui +2Concept DriftOnline Continual Learning

    University of Sydney, Sydney, Australia · Macquarie University, Sydney, Australia · Department of Informatics, University of Oslo, Oslo, Norway +2

  7. Joint End-to-End Training of Weighted BP Decoder Ensembles

    Oct 24, 2026Felix Krieg, Paul Bezner, Stephan ten BrinkError-Correcting CodesBelief Propagation

    Institute of Telecommunications, University of Stuttgart, Germany

  8. Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation

    Oct 22, 2026Gabriel Oliveira-Stahl, Anna Schroder, James Moggridge +6Medical Image SegmentationSegmentation Uncertainty Estimation

    Hawkes Institute, Department of Computer Science, University College London, UK · Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK · Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, UK +3

  9. Circuits of LLM Classifiers

    Oct 21, 2026Hadi Khalaf, Sajani Vithana, Kunal Talreja +3Large Language Model-Based Text ClassificationLanguage Model Ensembles

    Harvard School of Engineering and Applied Science · Kempner Institute for the Study of Natural & Artificial Intelligence · School of Electrical and Computer Engineering, Georgia Institute of Technology +2

  10. Logit Averaging During Training Sets a New State of the Art for Non-Invasive Speech BCI

    Oct 19, 2026Miran Özdogan, Gereon Elvers, Michael Bronstein +1Brain-Computer InterfacesNeural Decoding

    University of Oxford

  11. Dynamic self-ensemble on multi-exit residual vision transformer for small-data image classification

    Oct 17, 2026Stephen Ekaputra Limantoro, Yan-Kai Wang, Ching-Chun Huang +4Vision TransformerEnsemble Learning

    aDepartment of Electrical Engineering and Computer Science, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan · bDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan · cDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617, Taiwan +2

  12. A Jury, Not an Electorate: Label-Free Reliability Estimation for Ensembles of Language Models

    Oct 15, 2026Alessandro Rozza, Davide Mambelli, Steven Motta +1Language Model EnsemblesEnsemble Learning

    distributed cognition

  13. Exact power indices for plurality-voting ensembles

    Oct 13, 2026Ilie Sarpe, Theofanis Georgakopoulos, Aristides GionisShapley Value AttributionEnsemble Learning

    KTH Royal Institute of Technology, Stockholm, Sweden · Ericsson BCSS, Stockholm, Sweden · Digital Futures, Stockholm, Sweden

  14. Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion

    Oct 9, 2026Jingyan Xu, Yuanhong Wu, Samuel Yen-Chi Chen +3Ensemble LearningHybrid Quantum-Classical ML

    Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA · Wells Fargo, New York, NY, USA · Division of Computer Science, Mathematics and Science, St. John’s University, Queens, NY, USA

  15. CoGameReg: Cooperative-Game-Based Collaborative Semi-Supervised Regression for Seismic Reservoir Property Prediction

    Oct 9, 2026Zhiyu Guo, Jiwei Jia, Lei LiSemi-Supervised LearningEnsemble Learning

    School of Mathematics, Jilin University, Changchun, China · AI for Science and Engineering Center, Shenzhen Loop Area Institute, Shenzhen, China · BGP Inc., China National Petroleum Corporation, Zhuozhou, China +1

  16. Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models

    Oct 8, 2026Saurabh Mathur, Sahil Sidheekh, Bhavan Vasu +4Probabilistic LogicInductive Logic Programming

    Technische Universität Darmstadt · The University of Texas at Dallas · AT&T CDO +1

  17. Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits

    Oct 8, 2026Kanghui Ning, Marin Biloš, James T. Wilson +5Tabular Foundation ModelsInference-Time Scaling

    School of Computing, University of Connecticut, Storrs, USA. · Department of Machine Learning Research, Morgan Stanley, New York, USA.

  18. Boosting and the Expressive Power of Simple Weak Learners via the γγ-VC Dimension

    Oct 7, 2026Arthur da Cunha, Kasper Green Larsen, Liang-Yu ZouStatistical Learning TheoryWeak-to-Strong Generalization

    Aarhus University

  19. Detecting Nighttime Anomalies from NASA Black Marble Using a Generalized Spatio-Temporally Robust Framework of Machine Leaning Ensembles

    Oct 5, 2026Srija ChakrabortyEnsemble LearningAnomaly Detection

    Universities Space Research Association

  20. JudgeMoE: Distributional Aggregation for LLM-as-a-Judge

    Oct 5, 2026Yiqi Liu, Joseph James, Yang Wang +3LLM EvaluationLLM-as-a-Judge

    The University of Manchester · The University of Sheffield · University of Pittsburgh +1

  21. KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction

    Oct 5, 2026Rahul GoswamiEnsemble LearningSurvival Analysis

    Department of Mathematics, Indian Institute of Technology Guwahati

  22. TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations

    Oct 5, 2026Johann FaouziTime Series ClassificationEnsemble Learning

    Univ Rennes, Ensai, CNRS, CREST - UMR 9194, F-35000 Rennes, France.

  23. Vision Transformer Ensembles for Panoramic Street Segmentation

    Oct 5, 2026Yunus Serhat BıçakçıEnsemble LearningVision Transformer

    Department of Artificial Intelligence and Machine Learning, Faculty of Applied Sciences, Marmara University, Istanbul, Türkiye · Geospatial Data Science Group, School of Geographical and Earth Sciences, University of Glasgow, Glasgow, UK

  24. Self-Repairing Recurrent Ensembles for Real-Time Recovery from Distribution Shift

    Oct 2, 2026Julian Lemmel, Pedro D. Wendel Garcia, Taisuke Kobayashi +1RL ControlRecurrent Neural Networks

    Vienna University of Technology · Medical University of Vienna · National Institute of Informatics, Tokyo

  25. ProxyMOS: Label-Free Speech Quality Assessment by Multi-Teacher Distillation with Adaptive Routing

    Sep 30, 2026Maxim Trokunov, Kirill Borodin, Nikita Vasiliev +1Ensemble LearningTTS Evaluation

    Lab260, Yerevan, Armenia · BitmanagerAI, Dubai, UAE · MTUCI, Moscow, Russia

  26. Gestalt: a meta-foundation model for astronomy

    Sep 29, 2026Michael J. Smith, Shashwat SouravEnsemble LearningAI-Assisted Scientific Research

    AstroAI · Center for Astrophysics | Harvard & Smithsonian · UniverseTBD +1

  27. Latent Inference-Time Guidance of Time Series Foundation Models

    Sep 29, 2026Chloé Hashimoto-Cullen, Amaury Durand, Laurent Bozzi +3Time Series ForecastingInference-Time Guidance

    LPSM, Sorbonne Université · EDF R&D · UCL

  28. Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction

    Sep 29, 2026Shivang Chopra, Fotis Iliopoulos, Zsolt Kira +1Feature Interaction ModelingEnsemble Learning

    Georgia Institute of Technology · Google Research

  29. HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification

    Sep 29, 2026Simon Flügel, Till MossakowskiMulti-Label ClassificationExplainable Artificial Intelligence

    Institute for Computer Science, University of Osnabrück, Neuer Graben 29, 49074 Osnabrück, Germany

  30. LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction

    Sep 28, 2026Yinan Cheng, Lili ZhengEnsemble LearningAdaptive Sampling

    Department of Statistics University of Illinois Urbana-Champaign