The classifier judged this item not relevant to AI security, so it is left out of lists, feeds, the API and dataset releases. If that is wrong, report a correction on this page.
InfoResearchPeer-reviewed
Sparse Variational Student-t Processes for Heavy-Tailed Modeling
- Published
- Record updated
Summary
Researchers introduce sparse variational Student-t processes (SVTPs), a sparse inducing point framework that extends Student-t processes to large datasets, with two inference algorithms, SVTP-UB and SVTP-MC. On UCI and Kaggle datasets with outliers and heavy tails, SVTP reports up to 3× faster convergence and 40% lower prediction error than sparse GPs, while remaining tractable for datasets with over 200 000 samples.