34 citations · 57 across the 7 of their papers we have counts for
8 papers
Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization
Weichi Yao, Bianca Dumitrascu, Bryan R. Goldsmith +1
Active data acquisition is central to many learning and optimization tasks in deep neural networks, yet remains challenging because most approaches rely on predictive uncertainty e…
Dimensionless machine learning: Imposing exact units equivariance
Soledad Villar, Weichi Yao, David W. Hogg +2
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-co…
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…
Scalars are universal: Equivariant machine learning, structured like classical physics
Soledad Villar, David W. Hogg, Kate Storey-Fisher +2
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. Some of these f…
Dynamic estimation with random forests for discrete-time survival data
Hoora Moradian, Weichi Yao, Denis Larocque +2
Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available. In this paper, we in…
Ensemble methods for survival function estimation with time-varying covariates
Weichi Yao, Halina Frydman, Denis Larocque +1
Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of survival function. However, the traditional survival forests -…
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…
An Ensemble Method for Interval-Censored Time-to-Event Data
Weichi Yao, Halina Frydman, Jeffrey S. Simonoff
Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known…