GRAPHLCP improves localized conformal prediction on graphs by using feature-aware densification and Personalized PageRank kernels to incorporate topology for better coverage and efficiency.
A tutorial on principal component analysis
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
use method 1representative citing papers
CGM-JEPA learns transferable CGM representations via predictive self-supervised pretraining on unlabeled time series and cross-view distributional objectives, outperforming baselines on AUROC for insulin resistance and beta-cell dysfunction across modality shifts and cohorts.
citing papers explorer
-
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
GRAPHLCP improves localized conformal prediction on graphs by using feature-aware densification and Personalized PageRank kernels to incorporate topology for better coverage and efficiency.
-
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
CGM-JEPA learns transferable CGM representations via predictive self-supervised pretraining on unlabeled time series and cross-view distributional objectives, outperforming baselines on AUROC for insulin resistance and beta-cell dysfunction across modality shifts and cohorts.