7 citations · 15 across the 5 of their papers we have counts for
5 papers
Approximately Equivariant Graph Networks
Ningyuan Huang, Ron Levie, Soledad Villar
Graph neural networks (GNNs) are commonly described as being permutation equivariant with respect to node relabeling in the graph. This symmetry of GNNs is often compared to the tr…
From Local to Global: Spectral-Inspired Graph Neural Networks
Ningyuan Huang, Soledad Villar, Carey E. Priebe +4
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs are message-passing algorithms (MPNNs) that aggregate and combine signals in a…
MarkerMap: nonlinear marker selection for single-cell studies
Nabeel Sarwar, Wilson Gregory, George A Kevrekidis +2
Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features expl…
A polynomial-time relaxation of the Gromov-Hausdorff distance
Soledad Villar, Afonso S. Bandeira, Andrew J. Blumberg +1
The Gromov-Hausdorff distance provides a metric on the set of isometry classes of compact metric spaces. Unfortunately, computing this metric directly is believed to be computation…
Relax, no need to round: integrality of clustering formulations
Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar +3
We study exact recovery conditions for convex relaxations of point cloud clustering problems, focusing on two of the most common optimization problems for unsupervised clustering:…