论文标题
一种统治它们的配置？使用多目标贝叶斯优化的主题模型中的超参数转移
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
论文作者
论文摘要
主题模型是从文档收集中提取基本主题的统计方法。在执行主题建模时，用户通常希望相互连贯，彼此之间的多样性，并且构成了下游任务的良好文档表示（例如，文档分类）。在本文中，我们对三个知名主题模型进行了多目标超参数优化。所获得的结果揭示了不同目标的冲突性质，并且训练语料库特征对于超参数选择至关重要，这表明可以在数据集之间传输最佳的超参数配置。
Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, diverse between each other, and that constitute good document representations for downstream tasks (e.g. document classification). In this paper, we conduct a multi-objective hyperparameter optimization of three well-known topic models. The obtained results reveal the conflicting nature of different objectives and that the training corpus characteristics are crucial for the hyperparameter selection, suggesting that it is possible to transfer the optimal hyperparameter configurations between datasets.
