用黑箱优化自动选LDA主题数，效率远超传统方法
Topic Modelling Black Box Optimization
- 将主题数选择转为黑箱优化问题，每轮评估训练LDA并测困惑度
- 学习型优化器仅需1-3次评估就接近最优，传统算法需耗尽预算
- 适合需要快速调参的文本分析场景，尤其关注效率的研究者
在隐狄利克雷分配（LDA）中选择主题数 $T$ 是影响模型拟合与可解释性的关键设计决策。本文将 $T$ 的选择建模为离散黑箱优化问题，每次函数评估对应一次LDA训练与验证困惑度测量。在固定评估预算下，对比了四类优化器：两种人工设计的进化方法（遗传算法GA、进化策略ES）和两种学习型、可泛化的优化方法（偏好式泛化黑箱优化PABBO、尖锐感知黑箱优化SABBO）。实验表明，尽管四类方法最终均达到相近的困惑度水平，但学习型优化器显著更高效。SABBO通常仅需一次评估即可识别近优主题数，PABBO在数次评估内找到竞争力配置，而GA与ES需几乎耗尽预算才能逼近相同区域。
原文摘要 · Abstract (English)
Choosing the number of topics $T$ in Latent Dirichlet Allocation (LDA) is a key design decision that strongly affects both the statistical fit and interpretability of topic models. In this work, we formulate the selection of $T$ as a discrete black-box optimization problem, where each function evaluation corresponds to training an LDA model and measuring its validation perplexity. Under a fixed evaluation budget, we compare four families of optimizers: two hand-designed evolutionary methods - Genetic Algorithm (GA) and Evolution Strategy (ES) - and two learned, amortized approaches, Preferential Amortized Black-Box Optimization (PABBO) and Sharpness-Aware Black-Box Optimization (SABBO). Our experiments show that, while GA, ES, PABBO, and SABBO eventually reach a similar band of final perplexity, the amortized optimizers are substantially more sample- and time-efficient. SABBO typically identifies a near-optimal topic number after essentially a single evaluation, and PABBO finds competitive configurations within a few evaluations, whereas GA and ES require almost the full budget to approach the same region.
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