Events Calendar

NL Seminar- Exploring LDA: Parallel Inference and Model Selection

Speaker: Dehua Cheng, (USC/Melady)

Talk Title: Exploring LDA: Parallel Inference and Model Selection

Series: Natural Language Seminar

Abstract: Latent Dirichlet allocation (LDA) and its Bayesian nonparametric generalization hierarchical Dirichlet processes (HDP) have been proven successful in modeling large, complex, real-world domains. However, inference on LDA/HDP is challenging and it has received notable attention from the researchers. In this talk, we present two algorithmic advances for LDA/HDP inference by examining their mathematical properties. We will first present an effective parallel Gibbs sampling algorithm for LDA/HDP by exploring the equivalency between the Dirichlet-multinomial hierarchy and the Gamma-Poisson hierarchy. Secondly, we will show how to provably select the number of topics for LDA by studying the spectral space of its second order moments (bi-gram statistics).


Biography: Dehua Cheng is a third year Ph.D. student in the CS department at USC, advised by Professor Yan Liu. Prior to that, he received his B.S. degree in Mathematics and Physics from Tsinghua University, China. His research interests include randomized numerical algorithm in machine learning and parallel inference for probabilistic graphical model.

Host: Nima Pourdamghani and Kevin Knight

More Info: http://nlg.isi.edu/nl-seminar/

Location: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey

Audiences: Everyone Is Invited

Contact: Peter Zamar

Event Link: http://nlg.isi.edu/nl-seminar/