Ensemble Learning

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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

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

Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering
May 13, 2026Feijiang Li, Zhenxiong Li, Jieting Wang +3Ensemble Learning Clustering
Institute of Big Data Science and Industry, Shanxi University · Key Laboratory of Evolutionary Science Intelligence of Shanxi Province · School of Artificial Intelligence, Shanxi University
Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles
May 12, 2026Lara Sá Neves, Afonso Lourenço, Lizy K. John +1Concept Drift Ensemble Learning
GECAD, ISEP, Polytechnic of Porto, Portugal · The University of Texas at Austin, USA
Multi-Narrow Transformation as a Single-Model Ensemble: Boundary Conditions, Mechanisms, and Failure Modes
May 12, 2026Tatsuhito Hasegawa, Taisei TanakaNeural Network Generalization Ensemble Learning
Graduate School of Engineering, University of Fukui, Bunkyo, Fukui, 9108507, Fukui, Japan
Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
May 11, 2026Archie J. Huang, Dongdong Wang, Shaurya Agarwal +3Ensemble Learning Traffic Flow Estimation
Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada · Urban Artificial Intelligence Laboratory, University of Florida, Gainesville, USA · Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, USA
Efficient Ensemble Selection from Binary and Pairwise Feedback
May 10, 2026Tzeh Yuan Neoh, Nicholas Teh, Je Qin Chooi +2Pairwise Comparison Pairwise Preference Evaluation
Harvard University, USA · University of Oxford, UK
When Can Human-AI Teams Outperform Individuals? Tight Bounds with Impossibility Guarantees
May 9, 2026Dongxin Guo, Jikun Wu, Siu-Ming YiuHuman-AI Complementarity Ensemble Learning
The University of Hong Kong, Hong Kong, China · Stellaris AI Limited, Hong Kong, China · Brain Investing Limited, Hong Kong, China
PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams
May 8, 2026Pranavkumar Mallela, Vinay Kumar, Shashi Shekhar Jha +1Ensemble Learning Human-AI Decision Making
Indian Institute of Technology Ropar
Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
May 7, 2026Yutong Wang, Yannig Goude, Qiwei YaoEnsemble Learning Time Series Forecasting
Department of Statistics, LSE · Électricité de France R&D
PACE: Prune-And-Compress Ensemble Models
May 7, 2026Fabian Akkerman, Julien Ferry, Théo Guyard +1Model Compression Ensemble Learning
Industrial Engineering and Management Science University of Twente, Netherlands · SCALE-AI Chair and CIRRELT Polytechnique Montréal, Canada
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
May 7, 2026Yi-Siang Wang, Kuan-Yu Chen, Yu-Chen Den +1Ensemble Learning LLM Fine-Tuning
SinoPac Holdings
Enabling Federated Inference via Unsupervised Consensus Embedding
May 7, 2026Yui Hashimoto, Takayuki Nishio, Yuichi Kitagawa +1Ensemble Learning Privacy-Preserving ML
School of Engineering, Institute of Science Tokyo, Tokyo 152-8550, Japan · Research & Development Group, Hitachi Ltd., Tokyo 185-8601, Japan
Learned Neighbor Trust for Collaborative Deployment in Model-Agnostic Decentralized Learning
May 6, 2026Michael Lanier, Luise Ge, Sastry Kompella +1Ensemble Learning Decentralized Learning
Department of Computer Science Washington University in St. Louis and Nexcepta
ITBoost: Information-Theoretic Trust for Robust Boosting
May 6, 2026Ye Su, Longlong Zhao, Diego Garcia-Gil +4Gradient-Boosted Decision Trees Ensemble Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada +1
Perturb and Correct: Post-Hoc Ensembles using Affine Redundancy
May 2, 2026Eleanor QuintDistribution Shift Robustness Ensemble Learning
Department of Computing University of Nebraska–Lincoln Lincoln, NE 68508, USA
Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution
Apr 29, 2026T. Ansah-Narh, G. Y. Afrifa, J. B. Tandoh +6Ensemble Learning
A Divergence-Based Method for Weighting and Averaging Model Predictions
Apr 27, 2026Olav Benjamin VassendEnsemble Learning
University of Inland Norway
Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification
Apr 27, 2026June-Woo Kim, Miika Toikkanen, Heejoon Koo +3Ensemble Learning Audio Classification
Department of Electronic Engineering, Wonkwang University, Republic of Korea · RSC LAB, MODULABS, Republic of Korea · AICU Global Inc., Republic of Korea +1
A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts
Apr 25, 2026Ruyi Dai, Tingkwong Ng, Hao ChenHierarchical Classification Ensemble Learning
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China · Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China · Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China +2
Conformalized Super Learner
Apr 24, 2026Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez +1Ensemble Learning Conformal Prediction
Department of Mathematics King’s College London London, UK · Department of Statistics and Operations Research University of Granada Granada, Spain · Department of Statistical Science University College London London, UK
Promoting Simple Agents: Ensemble Methods for Event-Log Prediction
Apr 23, 2026Benedikt Bollig, Matthias Függer, Thomas Nowak +1Next-Action Prediction Ensemble Learning
Université Paris-Saclay, CNRS, ENS Paris-Saclay, LMF, Gif-sur-Yvette, France · Institut Universitaire de France, Paris, France · Direction Générale de l’Armement, Paris, France
A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry
Apr 22, 2026Lorenzo Riccardo Allegrini, Geremia PompeiEnsemble Learning Time-Series Anomaly Detection
ContinualIST, Pisa, Italy · University of Pisa, Department of Computer Science, Pisa, Italy
SDNGuardStack: An Explainable Ensemble Learning Framework for High-Accuracy Intrusion Detection in Software-Defined Networks
Apr 22, 2026Ashikuzzaman, Md. Saifuzzaman Abhi, Mahabubur Rahman +3Ensemble Learning Network Intrusion Detection
Dept. of CSE University of Barishal Barishal, Bangladesh · Dept. of CSE University of Scholars Dhaka, Bangladesh
Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
Apr 20, 2026Nicholas Williams, Alejandro SchulerCross-Fitting Debiased ML
Division of Biostatistics, University of California, Berkeley, California, U.S.A.
STRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction
Apr 19, 2026Swattik Maiti, Ritik Pratap Singh, Fardina Fathmiul AlamEnsemble Learning Tabular Prediction
Science Academy, University of Maryland, College Park MD, USA · Department of Computer Science, University of Maryland, College Park MD, USA
Collaborative Filtering Through Weighted Similarities of User and Item Embeddings
Apr 16, 2026Pedro R. Pires, Rafael T. Sereicikas, Gregorio F. Azevedo +1Ensemble Learning Recommender Systems
Federal University of São Carlos
MyoVision: A Mobile Research Tool and NEATBoost-Attention Ensemble Framework for Real Time Chicken Breast Myopathy Detection
Apr 15, 2026Chaitanya Pallerla, Siavash Mahmoudi, Dongyi WangEnsemble Learning Computational Imaging
Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, AR · Department of Food Science, University of Arkansas, Fayetteville, AR
Minimax Optimality and Spectral Routing for Majority-Vote Ensembles under Markov Dependence
Apr 15, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaEnsemble Learning Majority Voting
Department of Computer Science, Iowa State University · Department of Civil, Construction & Environmental Engineering, Iowa State University
Shapley Value-Guided Adaptive Ensemble Learning for Explainable Financial Fraud Detection with U.S. Regulatory Compliance Validation
Apr 14, 2026Mohammad Nasir Uddin, Md Munna AzizFinancial Fraud Detection Ensemble Learning
Visual Data Analyst and Applied AI Researcher, Westcliff University, Irvine, CA, USA · Data Analyst and Applied AI Researcher, Westcliff University, Irvine, CA, USA
Training-Free Model Ensemble for Single-Image Super-Resolution via Strong-Branch Compensation
Apr 13, 2026Gengjia Chang, Xining Ge, Weijun Yuan +4Image Super-Resolution Ensemble Learning
Hefei University of Technology · Hangzhou Dianzi University · Jinan University +2
Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning
Apr 13, 2026Ajinkya Mohgaonkar, Lukas Gosch, Mahalakshmi Sabanayagam +2Certified Robustness Neural Network Verification
School of Computation, Information and Technology, Technical University of Munich, Germany · Munich Data Science Institute, Technical University of Munich · Munich Center for Machine Learning, Germany +1

May 13, 2026J/K move · Enter open · S save

Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering

Feijiang Li, Zhenxiong Li, Jieting Wang, Zizheng Jiu+2

Institute of Big Data Science and Industry, Shanxi University · Key Laboratory of Evolutionary Science Intelligence of Shanxi Province · School of Artificial Intelligence, Shanxi University

Ensemble Learning Clustering Image Clustering Variance Reduction

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Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowledge to guide the clustering process. Recent approaches introduce semantic descriptions as prior information, most of which typically relying on matching-based techniques with predefined vocabularies. However, the limited matching space restricts their adaptability to downstream clustering tasks. Moreover, these methods primarily focus on reducing bias to improve performance, frequently overlooking the importance of variance reduction. To address these limitations, we propose GSEC (Image Clustering based on Generative Semantic Guidance and Bi-Layer Ensemble), a framework designed to reduce bias through generative semantic guidance and mitigate variance via ensemble learning. Our method employs Multimodal Large Language Models to generate semantic descriptions and derive image embeddings via weighted averaging. Additionally, a bi-layer ensemble strategy integrates cross-modal information through BatchEnsemble in the inner layer and aligns outputs via an alignment mechanism in the outer layer. Comparative experiments demonstrate that GSEC outperforms 18 state-of-the-art methods across six benchmark datasets, while further analysis confirms its effectiveness in simultaneously reducing both bias and variance. The code is available at https://github.com/2017LI/GSEC.git.