YASS: Yet Another Spike Sorter. - Jin Hyung Lee; David E. Carlson; Hooshmand Shokri Razaghi; Weichi Yao; Georges A. Goetz; Espen Hagen; Eleanor Batty; E. J. Chichilnisky; Gaute T. Einevoll; Liam Paninski (NIPS, 2017)

Experimental performance of graph neural networks on random instances of max-cut. - Weichi Yao; Afonso S. Bandeira; Soledad Villar (CoRR, 2019)

Scalars are universal: Equivariant machine learning, structured like classical physics. - Soledad Villar; David W. Hogg; Kate Storey-Fisher; Weichi Yao; Ben Blum-Smith (NeurIPS, 2021)

Scalars are universal: Gauge-equivariant machine learning, structured like classical physics. - Soledad Villar; David W. Hogg; Kate Storey-Fisher; Weichi Yao; Ben Blum-Smith (CoRR, 2021)

A simple equivariant machine learning method for dynamics based on scalars. - Weichi Yao; Kate Storey-Fisher; David W. Hogg; Soledad Villar (CoRR, 2021)

The passive symmetries of machine learning. - Soledad Villar; David W. Hogg; Weichi Yao; George A. Kevrekidis; Bernhard Schölkopf (CoRR, 2023)

Dimensionless machine learning: Imposing exact units equivariance. - Soledad Villar; Weichi Yao; David W. Hogg; Ben Blum-Smith; Bianca Dumitrascu (J. Mach. Learn. Res., 2023)

Towards fully covariant machine learning. - Soledad Villar; David W. Hogg; Weichi Yao; George A. Kevrekidis; Bernhard Schölkopf (Trans. Mach. Learn. Res., 2024)