1 code implementation • 16 Mar 2022 • Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hoffmann
To predict stragglers accurately and early without labeled positive examples or assumptions on latency distributions, this paper presents NURD, a novel Negative-Unlabeled learning approach with Reweighting and Distribution-compensation that only trains on negative and unlabeled streaming data.
no code implementations • 25 Mar 2021 • Hyebin Song, Garvesh Raskutti, Rebecca Willett
In a variety of settings, limitations of sensing technologies or other sampling mechanisms result in missing labels, where the likelihood of a missing label in the training set is an unknown function of the data.
no code implementations • 20 Mar 2018 • Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman
This work considers a high-dimensional regression setting in which a graph governs both correlations among the covariates and the similarity among regression coefficients -- meaning there is \emph{alignment} between the covariates and regression coefficients.