The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Statistical entity-topic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Structured correspondence topic models for mining captioned figures in biological literature
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Connections between the lines: augmenting social networks with text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient methods for topic model inference on streaming document collections
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Online multiscale dynamic topic models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic models with power-law using Pitman-Yor process
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Partially labeled topic models for interpretable text mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Conditional topical coding: an efficient topic model conditioned on rich features
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking trends: incorporating term volume into temporal topic models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent topic feedback for information retrieval
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic topic models with biased propagation on heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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We propose a novel collapsed variational Bayes (CVB) inference for the hierarchical Dirichlet process (HDP). While the existing CVB inference for the HDP variant of latent Dirichlet allocation (LDA) is more complicated and harder to implement than that for LDA, the proposed algorithm is simple to implement, does not require variance counts to be maintained, does not need to set hyper-parameters, and has good predictive performance.