3 papers
math.ST2026
Wasserstein Concentration of Empirical Measures for Dependent Data via the Method of Moments
Arash A. Amini, Luciano Vinas
We establish a general concentration result for the 1-Wasserstein distance between the empirical measure of a sequence of random variables and its expectation. Unlike standard resu…
cs.LG2024
Simple GNNs with Low Rank Non-parametric Aggregators
Luciano Vinas, Arash A. Amini
We revisit recent spectral GNN approaches to semi-supervised node classification (SSNC). We posit that state-of-the-art (SOTA) GNN architectures may be over-engineered for common S…
cs.LG2024
Sharp Bounds for Poly-GNNs and the Effect of Graph Noise
Luciano Vinas, Arash A. Amini
We investigate the classification performance of graph neural networks with graph-polynomial features, poly-GNNs, on the problem of semi-supervised node classification. We analyze…