21 citations · 35 across the 8 of their papers we have counts for
6 papers · 1 filter
Tensor learning with orthogonal, Lorentz, and symplectic symmetries
Wilson G. Gregory, Josué Tonelli-Cueto, Nicholas F. Marshall +2
Tensors are a fundamental data structure for many scientific contexts, such as time series analysis, materials science, and physics, among many others. Improving our ability to pro…
Is machine learning good or bad for the natural sciences?
David W. Hogg, Soledad Villar
Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in…
Deep Learning is Provably Robust to Symmetric Label Noise
Carey E. Priebe, Ningyuan Huang, Soledad Villar +2
Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. The…
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…
SqueezeFit: Label-aware dimensionality reduction by semidefinite programming
Culver McWhirter, Dustin G. Mixon, Soledad Villar
Given labeled points in a high-dimensional vector space, we seek a low-dimensional subspace such that projecting onto this subspace maintains some prescribed distance between point…
Monte Carlo approximation certificates for k-means clustering
Dustin G. Mixon, Soledad Villar
Efficient algorithms for -means clustering frequently converge to suboptimal partitions, and given a partition, it is difficult to detect -means optimality. In this paper, we…