Ensemble Learning

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18 papers in the last four weeks, up 38% on the four weeks before. 0.2% of all new papers.

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Latest papers 168

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Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
May 18, 2026Aditya Tanna, Yash Desai, Pratinav Seth +3Tabular Foundation Models Ensemble Learning
Lexsi Labs
Embedded ConvNet Ensembles: A Lightweight Approach to Recognize Arabic Handwritten Characters
May 18, 2026Mohsine El Khayati, Rachid Elouahbi, Abdelillah SemmaEfficient Neural Network Inference Ensemble Learning
Systems theory and informatics laboratory Moulay Ismail University of Meknes Meknes, Morocco · Laboratory of Computer Science and Applications Moulay Ismail University of Meknes Meknes, Morocco · Computer Science Dept. EST of Sidi Bennour Chouaib Doukkali University Eljadida, Morocco
Soft Learning
May 16, 2026Mohammed Aledhari, Ali Aledhari, Fatimah Aledhari +1Ensemble 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
Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version
May 15, 2026Hong-Phuc Phan, Tuan-Anh Vu, Tung Kieu +3Model Selection Unsupervised Learning
Department of Software Engineering, FPT University, Vietnam · Department of Information Technology, Can Tho University of Technology, Vietnam · Department of Computer Science, Aalborg University, Denmark +2
AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification
May 15, 2026Salma Hoque Talukdar Koli, Fahima Haque Talukder JelyPlant Disease Classification Ensemble Learning
RTM Al-Kabir Technical University, Sylhet-3100, Bangladesh · North East University Bangladesh, Sylhet, Bangladesh
LoMETab: Beyond Rank-1 Ensembles for Tabular Deep Learning
May 14, 2026Changryeol Choi, Hyewon Park, Yujin Kwon +1Ensemble Learning Low-Rank Adaptation
CJ Logistics
Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints
May 13, 2026Karan Sehgal, Khawar Naveed BhattiClass-Imbalanced Learning Gradient-Boosted Decision Trees
Kent Business School
Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach
May 13, 2026Ajinkya Naik, Chaitanya Garg, S. Akshay +2Ensemble Learning Formal Verification
Indian Institute of Technology Bombay, India · University of Toronto, Canada
Reducing cross-sample prediction churn in scientific machine learning
May 13, 2026Gordan Prastalo, Kevin Maik JablonkaEnsemble Learning Classification
Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Hahn-Meitner-Platz 1, 14109 Berlin, Germany · HIPOLE Jena (Helmholtz Institute for Polymers in Energy Applications Jena), Lessingstrasse 12–14, 077432026 Jena, Germany · Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstrasse 10, 07743 Jena, Germany +2
XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles
May 13, 2026Iakovos-Christos Zarkadis, Christos DouligerisEnsemble Learning Network Intrusion Detection
University of Piraeus Athens, Greece · Dept. of Informatics University of Piraeus Piraeus, Greece
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 YiuEnsemble Learning Human-AI Decision Making
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

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Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap

Aditya Tanna, Yash Desai, Pratinav Seth, Mohamed Bouadi+2

Lexsi Labs

Tabular Foundation Models Ensemble Learning Tabular Classification Ensemble Diversity

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Tabular foundation models (TFMs) now match or beat tuned gradient-boosted trees on a growing fraction of tabular tasks, but no single TFM wins on every dataset. Ensembling is the go to fix here, and it works less well than expected. Six modern TFMs form a near-redundant pool: their mean pairwise Q-statistic is

0.961

, close enough to

1

that any convex combination is bounded above. We benchmark six ensemble strategies over six TFMs on 153 OpenML classification tasks. The best ensemble, two-level cascade stacking, buys

+0.18\%

accuracy over the strongest single TFM at

253\times

the compute. A Friedman and Nemenyi analysis places three ensembles and the best base TFM in a single equivalence group; three other ensembles are significantly \emph{worse} than the best base. Stacking with a logistic-regression meta-learner is the most striking case: competitive accuracy and ROC-AUC, the worst log-loss rank among the ensembles. The meta-learner improves accuracy by sharpening class boundaries, which destroys calibration. We recommend greedy selection as the practical default.

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Latest papers 168