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|Abstract:||We present a truncation-free online variational inference algorithm for Bayesian nonparametric models. Unlike traditional (online) variational inference algorithms that require truncations for the model or the variational distribution, our method adapts model complexity on the fly. Our experiments for Dirichlet process mixture models and hierarchical Dirichlet process topic models on two large-scale data sets show better performance than previous online variational inference algorithms.|
|Citation:||Wang, Chong, and David M. Blei. "Truncation-free Online Variational Inference for Bayesian Nonparametric Models." Advances in Neural Information Processing Systems 25 (2012): pp. 413-421.|
|Pages:||413 - 421|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Advances in Neural Information Processing Systems|
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