16 citations · 48 across the 24 of their papers we have counts for
5 papers · 1 filter
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…
Compositional Score Modeling for Simulation-based Inference
Tomas Geffner, George Papamakarios, Andriy Mnih
Neural Posterior Estimation methods for simulation-based inference can be ill-suited for dealing with posterior distributions obtained by conditioning on multiple observations, as…
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…
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…
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster +9
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery…