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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.