4 papers
Localized Uncertainty Quantification in Random Forests via Proximities
Jake S. Rhodes, Scott D. Brown, J. Riley Wilkinson
In machine learning, uncertainty quantification helps assess the reliability of model predictions, which is important in high-stakes scenarios. Traditional approaches often emphasi…
Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification
Jake S. Rhodes, Adam G. Rustad, Sofia Pelagalli Maia +5
Missing data is a common problem in time series data. Most methods for imputation ignore label information pertaining to the time series even if that information exists. In this pa…
Guided Manifold Alignment with Geometry-Regularized Twin Autoencoders
Jake S. Rhodes, Adam G. Rustad, Marshall S. Nielsen +3
Manifold alignment (MA) involves a set of techniques for learning shared representations across domains, yet many traditional MA methods are incapable of performing out-of-sample e…
Random Forest-Supervised Manifold Alignment
Jake S. Rhodes, Adam G. Rustad
Manifold alignment is a type of data fusion technique that creates a shared low-dimensional representation of data collected from multiple domains, enabling cross-domain learning a…