Tag
REATS is a new approach that uses LLM reasoning to perform interpretable, sample-adaptive ensemble learning for time series forecasting. It combines textual and numerical features with chain-of-thought reasoning and a two-stage fine-tuning framework, outperforming competitive baselines on eight benchmarks.
The paper proposes intuitionistic fuzzy deep RVFL (IF-dRVFL) and ensemble deep RVFL (IF-edRVFL) frameworks that use sample neighborhood information to improve robustness against noise and outliers in classification tasks, outperforming existing SOTA fuzzy and non-fuzzy approaches on benchmark datasets.
This paper proposes a hierarchical, interpretable-by-design pivot selection model based on proximity trees and ensemble learning. It is data-modality-agnostic and demonstrates competitive results across tabular, text, image, and time-series datasets.
This paper proposes LFS-FRAME, a leakage-free stacked ensemble framework integrating Kolmogorov-Arnold Networks and XGBoost for robust multiclass classification, achieving 89.85% accuracy on major families and 81.74% on sub-families.
A critical reflection on whether using only three base models for millions of personal AI agents can produce genuinely diverse deliberation, arguing that correlated errors across models may create false unanimity and seeking operational metrics—drawn from ensemble learning—to measure true human representational diversity.
TabLoRA proposes a parameter-efficient neural ensemble method for large-scale tabular data by sharing a common backbone with predictor-specific low-rank adaptations, achieving competitive performance against GBDTs and deep learning baselines.
A critique of a popular quant thread selling a 77% win-rate random forest strategy, noting that the method is standard ensemble learning from a free Stanford lecture and that past performance does not guarantee future results.
This paper proposes SCBoost, a boosting framework that reduces learner redundancy by projecting residuals onto the orthogonal complement of previous predictions and using covariance-regularized weighting, with theoretical guarantees and strong empirical performance.
Introduces Simplex-Constrained Sparse Bagging (SCSB), a post-training framework that optimizes estimator weights over the probability simplex using out-of-bag samples, achieving up to 96% ensemble compression and improved calibration.
This paper presents WISE-HAR, an ensemble deep learning framework for WiFi-based human activity recognition, achieving robust performance and generalization across scenarios with minimal accuracy drops.
This paper proposes a deterministic climate-risk intelligence framework integrating orchestration, anomaly detection, and imbalance-aware ensemble learning for auditable ESG validation, addressing fragmented Scope 1-3 reporting data.
This paper presents MIPIAD, a multilingual defense framework against indirect prompt injection attacks using a hybrid of Qwen2.5-based classifiers and TF-IDF features with meta-ensemble learning. It demonstrates strong performance on English and Bangla benchmarks, achieving high F1 and AUROC scores while reducing cross-lingual gaps.
This academic paper presents a hierarchical ensemble pipeline for anomaly detection in ESA satellite telemetry, utilizing shapelet-based and statistical feature extraction to identify subtle anomalies in multivariate time-series data.
This paper details the YEZE system for SemEval-2026 Task 9, which detects online polarization in 22 languages using a heterogeneous ensemble of XLM-RoBERTa and mDeBERTa models.