Delta-AI: Local objectives for amortized inference in sparse graphical models

arXiv:2310.02423v2 Announce Type: replace
Abstract: We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\Delta$-AI). Our approach is based on the observation that when the sampling of variables in a PGM is seen as a sequence of actions taken by an agent, sparsity of the PGM enables local credit assignment in the agent's policy learning objective. This yields a local constraint that can be turned into a local loss in …

abstract algorithm arxiv call cs.lg delta inference observation sampling stat.ml type variables