MSHL learns higher-order group relations from incomplete spatiotemporal observations via adaptive multi-scale hypergraph Laplacians and a safe neural refinement stage that improves imputation when structure is present.
Greedy function approximation: a gradient boosting machine.Annals of statistics, pages 1189–1232
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
years
2026 3representative citing papers
Directional Chebyshev harmonics enable spectral path regression for tabular data with closed-form training, competitive accuracy, and explicit interpretability.
New imbalanced NGS dataset pairs QC-34 features with ENCODE blocklist features on the same human and mouse samples to study quality control prediction.
citing papers explorer
-
Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
MSHL learns higher-order group relations from incomplete spatiotemporal observations via adaptive multi-scale hypergraph Laplacians and a safe neural refinement stage that improves imputation when structure is present.
-
Spectral Path Regression: Directional Chebyshev Harmonics for Interpretable Tabular Learning
Directional Chebyshev harmonics enable spectral path regression for tabular data with closed-form training, competitive accuracy, and explicit interpretability.
-
An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing
New imbalanced NGS dataset pairs QC-34 features with ENCODE blocklist features on the same human and mouse samples to study quality control prediction.