Variational Inference via Entropic Transport Descent
Particle-based variational inference (ParVI) methods approximate an intractable target distribution by evolving an ensemble of interacting samples. Existing approaches rely predominantly on kernel-based repulsion (e.g., SVGD), which suffers from variance collapse in high dimensions and mode collapse on multimodal targets -- pathologies caused by the absence of global transport structure. We introduce entropic transport descent (ETD), a ParVI family that frames each particle update as an entropy-regularized optimal transport problem. Derived from the JKO proximal scheme by lifting to the space of couplings and relaxing via the KL chain rule, each ETD iteration reduces to a Sinkhorn computa...
| Authors | Vincent Pacelli, Akash Ratheesh, Evangelos Theodorou |
|---|---|
| Primary category | cs.LG |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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