Variational inference has recently emerged as a popular alternative to Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference. A core idea of variational inference is to trade statistical accuracy for computational efficiency. It aims to approximate the posterior, as opposed to targeting the exact posterior as in MCMC. Approximating the exact posterior by a restricted inferential model (a.k.a. variational approximating family) reduces computation costs but sacrifices its statistical accuracy. In this work, we develop a theoretical characterization of this statistical-computational tradeoff in variational inference. We focus on a case study of Bayesian linear regression using inferential models (a.k.a. variational approximating families) with different degrees of flexibility. From a computational perspective, we find that less flexible variational families speed up computation. They reduce the variance in stochastic optimization and in turn, accelerate convergence. From a statistical perspective, however, we find that less flexible families suffer in approximation quality, but provide better statistical generalization. This is joint work with Kush Bhatia, Nikki Kuang, and Yi-an Ma.
Speaker Bio: Yixin Wang is an LSA Collegiate Fellow in Statistics at the University of Michigan. She works in the fields of Bayesian statistics, machine learning, and causal inference. Previously, she was a postdoctoral researcher with Professor Michael Jordan at the University of California, Berkeley. She completed her PhD in statistics at Columbia, advised by Professor David Blei, and her undergraduate studies in mathematics and computer science at the Hong Kong University of Science and Technology. Her research has received several awards, including the INFORMS data mining best paper award, Blackwell-Rosenbluth Award from the junior section of ISBA, student paper awards from ASA Biometrics Section and Bayesian Statistics Section, and the ICSA conference young researcher award.