= David Duvenaud
{c}
<David Duvenaud> coauthored the 2018 study of inference suboptimality with <Chris Cremer> and <Xuechen Li>. Their analysis distinguishes distribution-family limitations from a shared inference rule's shortcomings.<Inference Suboptimality in Variational Autoencoders by Chris Cremer and others>[Title-page byline; Table1 and §3.1]{ref}
An original diagnostic example makes that distinction numerical. Fix the generative model and one observation. Suppose its exact log evidence is $-5$, the globally best <evidence lower bound> within a chosen family is $-5.2$, and a <recognition network> returns a distribution with bound $-5.9$. The <variational approximation gap> is $0.2$ nat; the <amortization gap> is $0.7$ nat. They sum to the total $0.9$-nat inference deficit.
These stipulated exact values isolate the diagnostic, rather than reproducing experimental measurements. Improving only the shared rule could recover at most $0.7$ nat within that family. Comparing a different generative model would change the target <posterior distribution> and would no longer isolate the same gap. This is a contribution of the joint paper; its byline does not specify individual responsibility for each derivation.
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