no code implementations • 31 Jan 2023 • Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf
We discuss links to causal modeling, and argue that the implementation of passive symmetries is particularly valuable when the goal of the learning problem is to generalize out of sample.
1 code implementation • 2 Apr 2022 • Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu
Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings.
2 code implementations • 7 Oct 2021 • Weichi Yao, Kate Storey-Fisher, David W. Hogg, Soledad Villar
Physical systems obey strict symmetry principles.
2 code implementations • NeurIPS 2021 • Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith
There has been enormous progress in the last few years in designing neural networks that respect the fundamental symmetries and coordinate freedoms of physical law.
1 code implementation • 1 Mar 2021 • Hoora Moradian, Weichi Yao, Denis Larocque, Jeffrey S. Simonoff, Halina Frydman
Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available.
Methodology Applications
2 code implementations • 31 May 2020 • Weichi Yao, Halina Frydman, Denis Larocque, Jeffrey S. Simonoff
We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated L2 difference between the true and estimated survival functions.
no code implementations • 15 Aug 2019 • Weichi Yao, Afonso S. Bandeira, Soledad Villar
In particular we consider Graph Neural Networks (GNNs) -- a class of neural networks designed to learn functions on graphs -- and we apply them to the max-cut problem on random regular graphs.
1 code implementation • NeurIPS 2017 • 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
Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data.