Papers › Random Function Priors for Correlation Modeling
Random Function Priors for Correlation Modeling
Aonan Zhang, John Paisley
The likelihood model of high dimensional data Xₙ can often be expressed as p(Xₙ|Zₙ,θ), where θ:=(θₖ)_(k∈[K]) is a collection of hidden features shared across objects, indexed by n, and Zₙ is a non-negative factor loading vector with K entries where Zₙₖ indicates the strength of θₖ used to express Xₙ. In this paper, we introduce random function priors for Zₙ for modeling correlations among its K dimensions Zₙ₁ through Z_(nK), which we call \textit{population random measure embedding} (PRME). Our model can be viewed as a generalized paintbox model~\cite{Broderick13} using random functions, and can be learned efficiently with neural networks via amortized variational inference. We derive our Bayesian nonparametric method by applying a representation theorem on separately exchangeable discrete random measures.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections