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Found 3 papers, 1 papers with code
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data.
Multi-task Learning for Aggregated Data using Gaussian Processes
Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.
Convening during COVID-19: Lessons learnt from organizing virtual workshops in 2020
This report is an account of the authors' experiences as organizers of WiML's "Un-Workshop" event at ICML 2020.
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