5 papers
Towards a Taxonomy of Graph Learning Datasets
Renming Liu, Semih Cantürk, Frederik Wenkel +10
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN mo…
Unbiasing Procedures for Scale-invariant Multi-reference Alignment
Matthew Hirn, Anna Little
This article discusses a generalization of the 1-dimensional multi-reference alignment problem. The goal is to recover a hidden signal from many noisy observations, where each nois…
Balancing Geometry and Density: Path Distances on High-Dimensional Data
Anna Little, Daniel McKenzie, James Murphy
New geometric and computational analyses of power-weighted shortest-path distances (PWSPDs) are presented. By illuminating the way these metrics balance density and geometry in the…
Wavelet invariants for statistically robust multi-reference alignment
Matthew Hirn, Anna Little
We propose a nonlinear, wavelet based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference a…
Exact Cluster Recovery via Classical Multidimensional Scaling
Anna Little, Yuying Xie, Qiang Sun
Classical multidimensional scaling is an important dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides…