PACE通过交替剪枝与生成，高效压缩集成模型。
PACE: Prune-And-Compress Ensemble Models
- 先生成新模型提升多样性，再剪枝保留关键成员。
- 在保持原模型性能前提下，压缩后模型规模更小。
- 适合需要可解释性与部署效率的场景。
集成模型在预测任务中表现卓越，但通常需聚合大量弱学习器，导致部署困难、可解释性差，且不利于鲁棒性验证等下游任务。现有解决方案主要分为两类：剪枝（移除冗余学习器）和压缩（从头生成新学习器）。本文提出PACE框架，采用两阶段策略：第一阶段通过理论支撑的方法主动生成新学习器，增强初始集成模型的多样性；当无法找到更多相关学习器时，进入第二阶段进行剪枝。在整个过程中，PACE能对压缩后的模型与原集成模型之间的忠实度进行精细控制。实验表明，该方法在性能上优于已有剪枝与压缩方法，同时提供可证明的忠实度保证。
原文摘要 · Abstract (English)
Ensemble models achieve state-of-the-art performance on prediction tasks, but usually require aggregating a large number of weak learners. This can hinder deployment, interpretability, and downstream tasks such as robustness verification. Remedies to this issue fall into two main camps: pruning, which discards redundant learners, and compression, which generates new ones from scratch. We introduce PACE, a framework that interleaves these paradigms in a two-phase strategy. First, new learners are actively generated via a theoretically grounded procedure to enhance the diversity of the initial ensemble. When no more relevant learners can be found, a second phase of pruning is performed on this enriched ensemble. During both operations, PACE allows fine control on the faithfulness to the original ensemble. Experiments show that our method outperforms prior pruning and compression methods while offering principled control of faithfulness guarantees.
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