动态调整模型权重，让不同专家协作更默契、表现更稳。
EARCP: Self-Regulating Coherence-Aware Ensemble Architecture for Sequential Decision Making -- Ensemble Auto-Regule par Coherence et Performance
- 根据模型表现和共识度实时调节权重，避免依赖固定组合。
- 理论证明误差随时间增长缓慢，实测在预测任务中效果更优。
- 适合需要持续学习的时序任务，如金融、行为识别等场景。
我们提出EARCP（基于一致性和性能自调节的集成架构），一种新型集成框架，通过结合模型个体表现与相互一致性，动态调整异构专家模型的权重。不同于依赖静态或离线训练组合的传统方法，EARCP采用在线学习机制，持续优化权重，在高性能模型利用与基于共识信号的探索之间取得平衡。该架构融合乘法权重更新理论与新颖的一致性正则项，理论上可保证亚线性后悔界O(√(T log M))，且在非平稳环境中表现出强鲁棒性。我们在时间序列预测、活动识别和金融预测等顺序决策任务上进行了实证评估，验证了其有效性。该框架为具有时序依赖性的任意领域提供通用集成学习方案。代码已开源，可通过GitHub及PyPI获取。
原文摘要 · Abstract (English)
We present EARCP (Ensemble Auto-Régulé par Cohérence et Performance), a novel ensemble architecture that dynamically weights heterogeneous expert models based on both their individual performance and inter-model coherence. Unlike traditional ensemble methods that rely on static or offline-learned combinations, EARCP continuously adapts model weights through a principled online learning mechanism that balances exploitation of high-performing models with exploration guided by consensus signals. The architecture combines theoretical foundations from multiplicative weight update algorithms with a novel coherence-based regularization term, providing both theoretical guarantees through regret bounds and practical robustness in non-stationary environments. We formalize the EARCP framework, prove sublinear regret bounds of O(sqrt(T log M)) under standard assumptions, and demonstrate its effectiveness through empirical evaluation on sequential prediction tasks including time series forecasting, activity recognition, and financial prediction. The architecture is designed as a general-purpose framework applicable to any domain requiring ensemble learning with temporal dependencies. An open-source implementation is available at https://github.com/Volgat/earcp and via PyPI (pip install earcp).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。