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Found 3 papers, 1 papers with code
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction
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.
Prediction in the presence of response-dependent missing labels
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.
Graph-based regularization for regression problems with alignment and highly-correlated designs
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.
