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cs.LG2022
Joint control variate for faster black-box variational inference
Xi Wang, Tomas Geffner, Justin Domke
Black-box variational inference performance is sometimes hindered by the use of gradient estimators with high variance. This variance comes from two sources of randomness: Data sub…
cs.LG2022
Langevin Diffusion Variational Inference
Tomas Geffner, Justin Domke
Many methods that build powerful variational distributions based on unadjusted Langevin transitions exist. Most of these were developed using a wide range of different approaches a…
cs.LG2022
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
Tomas Geffner, Justin Domke
Hierarchical models represent a challenging setting for inference algorithms. MCMC methods struggle to scale to large models with many local variables and observations, and variati…