Statistical methods for network data often parameterize the edge-probability by attributing latent traits such as block structure to the vertices and assume exchangeability in the sense of the Aldous-Hoover representation theorem.

The goal is to find the model, i.e. hyper-parameters, with the highest validation accuracy.

The goal is to find the model, i.e. hyper-parameters, with the highest validation accuracy.

Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable.

Therefore, their lower bound is not comparable with our achievable upper bounds.

The proof involves 3 steps: 1. A null 5 (with a constant A > 2ξ) the GW formulation involves pairs of points. This yields the following cases: Case 1: a > 0. In that case, φ (γ) is a convex function, whose minimum on [0, 1] is reached for γ Using the development in Section 1.2.2 of the supplemental, we can establish that The partial-OT computation is based on a augmented problem with a dummy point and, as such, is convex. On the contrary, the GW problem is non-convex and, although the algorithm is proved to converge, there is no guarantee that the global optimum is reached. The quality of the solution is therefore highly dependent on the initialization.

This supplementary document is structured as follows.

Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable.