CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models
This work addresses a specific problem in Bayesian nonparametric modeling for researchers, offering incremental improvements over traditional methods.
The paper tackled the limitations of existing variational inference methods for hierarchical Bayesian nonparametric models, which fail to capture latent variable correlations and true posterior dimensions, by proposing CATVI, which achieved lower perplexity and clearer topic clustering in empirical studies on large datasets.
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.