Scalable algorithms for latent variable models in machine learning
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Latent variable modeling (LVM) is a popular approach in many machine learning applications, such as recommender systems and topic modeling, due to its ability to succinctly represent data, even in the presence of several missing entries. Existing learning methods for LVMs, while attractive, are infeasible for the large-scale datasets required in modern big data applications. In addition, such applications often come with various types of side information such as the text description of items and the social network among users in a recommender system. In this thesis, we present scalable learning algorithms for a wide range of latent variable models such as low-rank matrix factorization and latent Dirichlet allocation. We also develop simple but effective techniques to extend existing LVMs to exploit various types of side information and make better predictions in many machine learning applications such as recommender systems, multi-label learning, and high-dimensional time-series prediction. In addition, we also propose a novel approach for the maximum inner product search problem to accelerate the prediction phase of many latent variable models.