Faithful Inversion of Generative Models for Effective Amortized Inference
This addresses a bottleneck in amortized inference for probabilistic modeling, offering an incremental improvement over existing methods.
The paper tackles the problem of inverting dependency structures in generative models for amortized inference, introducing an algorithm that produces minimally faithful inverses and empirically shows they lead to better inference amortization than heuristic approaches.
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently. Generally, they require the inversion of the dependency structure in the generative model, as the modeller must learn a mapping from observations to distributions approximating the posterior. Previous approaches have involved inverting the dependency structure in a heuristic way that fails to capture these dependencies correctly, thereby limiting the achievable accuracy of the resulting approximations. We introduce an algorithm for faithfully, and minimally, inverting the graphical model structure of any generative model. Such inverses have two crucial properties: (a) they do not encode any independence assertions that are absent from the model and; (b) they are local maxima for the number of true independencies encoded. We prove the correctness of our approach and empirically show that the resulting minimally faithful inverses lead to better inference amortization than existing heuristic approaches.