论文标题
CATVI：层次贝叶斯非参数模型的条件和自适应截断的变分推断
CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models
论文作者
论文摘要
层次贝叶斯非参数模型的当前变异推理方法既不能表征由于平均场设置引起的潜在变量之间的相关结构，也不能因为通用截断而推断真正的后尺寸。为了克服这些局限性，我们提出了条件和自适应截断的变分推理方法（CATVI），通过将非参数证据下限和将蒙特卡洛整合到变异推理框架中。 Catvi比传统方法具有多种优势，包括变异和真实后代之间的差异较小，降低了拟合不足或过度拟合的风险，并提高了预测准确性。对三个大数据集的实证研究表明，在贝叶斯非参数主题模型中应用的CATVI基本上优于竞争模型，从而提供了较低的困惑和更清晰的主题字群集。
Current variational inference methods for hierarchical Bayesian nonparametric models can neither characterize the correlation structure among latent variables due to the mean-field setting, nor infer the true posterior dimension because of the universal truncation. To overcome these limitations, we propose the conditional and adaptively truncated variational inference method (CATVI) by maximizing the nonparametric evidence lower bound and integrating Monte Carlo into the variational inference framework. CATVI enjoys several advantages over traditional methods, including a smaller divergence between variational and true posteriors, reduced risk of underfitting or overfitting, and improved prediction accuracy. Empirical studies on three large datasets reveal that CATVI applied in Bayesian nonparametric topic models substantially outperforms competing models, providing lower perplexity and clearer topic-words clustering.
