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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Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models
Oct 8, 2026Saurabh Mathur, Sahil Sidheekh, Bhavan Vasu +4Probabilistic Logic Inductive Logic Programming
Technische Universität Darmstadt · The University of Texas at Dallas · AT&T CDO +1
Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits
Oct 8, 2026Kanghui Ning, Marin Biloš, James T. Wilson +5Tabular Foundation Models Inference-Time Scaling
School of Computing, University of Connecticut, Storrs, USA. · Department of Machine Learning Research, Morgan Stanley, New York, USA.
Boosting and the Expressive Power of Simple Weak Learners via the $γ$ -VC Dimension
Oct 7, 2026Arthur da Cunha, Kasper Green Larsen, Liang-Yu ZouStatistical Learning Theory Weak-to-Strong Generalization
Aarhus University
Detecting Nighttime Anomalies from NASA Black Marble Using a Generalized Spatio-Temporally Robust Framework of Machine Leaning Ensembles
Oct 5, 2026Srija ChakrabortyEnsemble Learning Anomaly Detection
Universities Space Research Association
JudgeMoE: Distributional Aggregation for LLM-as-a-Judge
Oct 5, 2026Yiqi Liu, Joseph James, Yang Wang +3LLM Evaluation LLM-as-a-Judge
The University of Manchester · The University of Sheffield · University of Pittsburgh +1
KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction
Oct 5, 2026Rahul GoswamiEnsemble Learning Survival Analysis
Department of Mathematics, Indian Institute of Technology Guwahati
TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations
Oct 5, 2026Johann FaouziTime Series Classification Ensemble Learning
Univ Rennes, Ensai, CNRS, CREST - UMR 9194, F-35000 Rennes, France.
Vision Transformer Ensembles for Panoramic Street Segmentation
Oct 5, 2026Yunus Serhat BıçakçıEnsemble Learning Vision 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
ProxyMOS: Label-Free Speech Quality Assessment by Multi-Teacher Distillation with Adaptive Routing
Sep 30, 2026Maxim Trokunov, Kirill Borodin, Nikita Vasiliev +1Ensemble Learning TTS Evaluation
Lab260, Yerevan, Armenia · BitmanagerAI, Dubai, UAE · MTUCI, Moscow, Russia
Gestalt: a meta-foundation model for astronomy
Sep 29, 2026Michael J. Smith, Shashwat SouravEnsemble Learning AI-Assisted Scientific Research
AstroAI · Center for Astrophysics | Harvard & Smithsonian · UniverseTBD +1
Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction
Sep 29, 2026Shivang Chopra, Fotis Iliopoulos, Zsolt Kira +1Feature Interaction Modeling Ensemble Learning
Georgia Institute of Technology · Google Research
ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding
Sep 27, 2026Abdul Basit, Saim Rehman, Muhammad ShafiqueCross-Subject EEG Decoding MI Classification
eBRAIN Lab, Division of Engineering New York University (NYU) Abu Dhabi, Abu Dhabi, UAE
Median Temporal Ensembling: Training-Free Robust Aggregation for Action-Chunked Visuomotor Policies
Sep 22, 2026Yuhang JiangEnsemble Learning Adversarial Robustness
University of Trento, Italy
CORE-STACK+: Meta-Learning for Deep Stacked Generalization
Sep 22, 2026Noor Islam S. MohammadEnsemble Learning Meta-Learning
Department of Computer Science Istanbul Technical University Maslak, Istanbul 34469, TR
A Distributional Optimisation Perspective on Combining Models in Deep Learning
Sep 21, 2026Congye Wang, Yan Lin, Zheyang Shen +2Ensemble Learning Deep Ensembles
Newcastle University, UK
Entropy-aware logistic regression for fusion of large-scale speaker recognition systems
Sep 20, 2026Pierre-Michel Bousquet, Mickael RouvierLogistic Regression Speaker Verification
LIA, Avignon University, France
On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm
Sep 15, 2026Julien Bastian, Benjamin Leblanc, Pascal Germain +4PAC-Bayesian Generalization Bounds Ensemble Learning
LabHC · Universit´e Jean Monnet Saint-´Etienne, CNRS, Institut d Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France · LabHC, UJM, MALICE +6
Ensemble Complexity in Photovoltaic Forecasting
Sep 14, 2026Sun Ze, Zhou Liguo, Xu Yuqing +2Ensemble Learning Solar Power Forecasting
aSchool of Computer Science and Technology, Huaibei Normal University, China
Horizon-specific Expert Fusion for Photovoltaic Power Forecasting
Sep 14, 2026Xu Yuqing, Zhou Liguo, Sun Ze +2Ensemble Learning Solar Power Forecasting
School of Computer Science and Technology, Huaibei Normal University, China
Beyond Argmax: A Mechanistic Study of Semantic Retention in Frozen Foundation-Model Composition for Generalized Few-Shot 3D Segmentation
Sep 14, 2026Silas Kwabla Gah, Ebenezer Owusu3D Foundation Models Ensemble Learning
Department of Computer Science University of Ghana, Legon
SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling
Sep 14, 2026Junquan Gu, Shibo Cui, Xiangfeng Luo +1LLM Self-Correction Ensemble Learning
A Multi-View and Confusion-Guided Ensemble Framework for Robust Synthetic Image Attribution
Sep 11, 2026Zuomin QuEnsemble Learning AI-Generated Image Detection
State Key Laboratory of HVDC, China Southern Power Grid Electric Power Research Institute, China
Reliability-Aware Hybrid-K Ensemble Selection for Cervical Cytology Classification: Integrating Discrimination, Calibration, and Selective Prediction
Sep 10, 2026Nisreen Albzour, Sarah S. LamComputational Pathology Ensemble Learning
School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
Algorithmic stability via ensembling
Sep 9, 2026Rina Foygel Barber, Richard J. SamworthEnsemble Learning Algorithmic Stability
University of Chicago · University of Cambridge
Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models
Sep 8, 2026Simon Adamov, Oliver Fuhrer, Reto Knutti +1Uncertainty Quantification Ensemble Learning
Federal Office for Meteorology and Climatology MeteoSwiss, Zurich, Switzerland · Institute for Atmospheric and Climate Science (IAC), ETH Zurich, Zurich, Switzerland · Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts
Sep 7, 2026Mária LakatosEnsemble Learning Probabilistic Forecasting
Faculty of Informatics, University of Debrecen, Hungary
An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data
Sep 3, 2026Lucas de Oliveira Cunha, Joelton Deonei Gotz, Paulo Lisboa de Almeida +1Ensemble Learning Autonomous Parking
Universidade Federal do Paraná, Department of Informatics, Curitiba, Paraná, Brazil · Pontifícia Universidade Católica do Paraná, Graduate Program in Informatics (PPGIa), Curitiba, Paraná, Brazil
Orthogonal Ensembles and Tested Explanations for Performer-Independent Body-Motion Emotion Recognition
Sep 2, 2026Naoto Nishida, Yoshio IshiguroEmotion Recognition Ensemble Learning
The University of Tokyo Tokyo, Japan
Measuring consistency via ensemble margin and local prediction variability: Auditing decision systems in the presence of predictive multiplicity
Sep 1, 2026Sinjini Banerjee, Tim Marrinan, Anand D. SarwateSelective Prediction Ensemble Learning
Department of Electrical and Computer Engineering, Rutgers University · Pacific Northwest National Lab
OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques
Aug 31, 2026Hamed Babaei Giglou, Sören Auer, Peio Popov +2Ensemble Learning
TIB Leibniz Information Centre for Science and Technology, Hannover, Germany · L3S Research Center, Leibniz University of Hannover, Hannover, Germany · Graphwise, Sofia, Bulgaria +1

Oct 8, 2026, cs.AIJ/K move · Enter open · S save

Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models

Saurabh Mathur, Sahil Sidheekh, Bhavan Vasu, Farbod Tavakkoli+3

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

Probabilistic Logic Inductive Logic Programming LLM-Based Program Synthesis Ensemble Learning

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Declarative logic programs offer a powerful and interpretable abstraction for encoding relational structure and neurosymbolic reasoning, by expressing dependencies as weighted compositional rules. However, inducing them from data remains fundamentally hard, bottlenecked by the combinatorial explosion of symbolic search spaces. LLMs have recently emerged as powerful hypothesis generators, but when used in isolation, they lack the capacity to do systematic inductive reasoning needed to reliably synthesize valid programs that fit complex relational distributions. We introduce grasp (Gradient-boosted Synthesis of Probabilistic logic programs), a neurosymbolic framework that casts relational structure learning as functional gradient boosting in which the weak learner is a first-order rule and the intractable inner search is delegated to an LLM proposal oracle. We evaluate grasp on four relational benchmarks spanning molecular toxicity prediction (Tox21), mutagenesis, and citation matching (Cora), and show that it improves over purely symbolic, neural, and LLM-based baselines, while producing interpretable weighted rule ensembles. By replacing combinatorial search with gradient-guided LLM hypothesis generation, grasp retains boosting guarantees without sacrificing the transparency of symbolic outputs.

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Latest papers 168