8 papers
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…
Laplacian Kernelized Bandit
Shuang Wu, Arash A. Amini
We study multi-user contextual bandits where users are related by a graph and their reward functions exhibit both non-linear behavior and graph homophily. We introduce a principled…
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…
Graph Neural Thompson Sampling
Shuang Wu, Arash A. Amini
We consider an online decision-making problem with a reward function defined over graph-structured data. We formally formulate the problem as an instance of graph action bandit. We…
Network two-sample test for block models
Chung Kyong Nguen, Oscar Hernan Madrid Padilla, Arash A. Amini
We consider the two-sample testing problem for networks, where the goal is to determine whether two sets of networks originated from the same stochastic model. Assuming no vertex c…
Step and Smooth Decompositions as Topological Clustering
Luciano Vinas, Arash A. Amini
We investigate a class of recovery problems for which observations are a noisy combination of continuous and step functions. These problems can be seen as non-injective instances o…
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…
Approximate Identification of the Optimal Epidemic Source in Complex Networks
S. Jalil Kazemitabar, Arash A. Amini
We consider the problem of identifying the source of an epidemic, spreading through a network, from a complete observation of the infected nodes in a snapshot of the network. Previ…