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Approximate Inference for High-Dimensional Latent Variable Models

Ph. D. Defense
Speaker Name
Zilong Tan
3232 French Family Science Center
Date and Time

Latent variable models are widely used in applications ranging from natural language processing to recommender systems. Exact inference using maximum likelihood for these models is generally NP-hard, and computationally prohibitive on big and/or high-dimensional data. This has motivated the development of approximate inference methods that balance between computational complexity and statistical efficiency. Understanding the computational and statistical tradeoff is important for analyzing approximate inference approaches as well as designing new ones.