21 citations · 35 across the 8 of their papers we have counts for
11 papers
A simple equivariant machine learning method for dynamics based on scalars
Weichi Yao, Kate Storey-Fisher, David W. Hogg +1
Physical systems obey strict symmetry principles. We expect that machine learning methods that intrinsically respect these symmetries should have higher prediction accuracy and bet…
Fitting very flexible models: Linear regression with large numbers of parameters
David W. Hogg, Soledad Villar
There are many uses for linear fitting; the context here is interpolation and denoising of data, as when you have calibration data and you want to fit a smooth, flexible function t…
MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data
Andrew J. Blumberg, Mathieu Carriere, Michael A. Mandell +2
Comparing and aligning large datasets is a pervasive problem occurring across many different knowledge domains. We introduce and study MREC, a recursive decomposition algorithm for…
Can Graph Neural Networks Count Substructures?
Zhengdao Chen, Lei Chen, Soledad Villar +1
The ability to detect and count certain substructures in graphs is important for solving many tasks on graph-structured data, especially in the contexts of computational chemistry…
Experimental performance of graph neural networks on random instances of max-cut
Weichi Yao, Afonso S. Bandeira, Soledad Villar
This note explores the applicability of unsupervised machine learning techniques towards hard optimization problems on random inputs. In particular we consider Graph Neural Network…
Utility Ghost: Gamified redistricting with partisan symmetry
Dustin G. Mixon, Soledad Villar
Inspired by the word game Ghost, we propose a new protocol for bipartisan redistricting in which partisan players take turns assigning precincts to districts. We prove that in an i…