It Just Takes Two: Scaling Amortized Inference to Large Sets

ArXi:2605.07972v1 Announce Type: cross Neural posterior estimation has emerged as a powerful tool for amortized inference, with growing adoption across scientific and applied domains. In many of these applications, the conditioning variable is a set of observations whose elements depend not only on the target but also on unknown factors shared across the set. Optimal inference therefore requires treating the set jointly, which in turn requires