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cs.LG2025
Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
Fred Xu, Thomas Markovich
Graph Neural Networks have achieved impressive results across diverse network modeling tasks, but accurately estimating uncertainty on graphs remains difficult, especially under di…
cs.LG2025
When to retrain a machine learning model
Regol Florence, Schwinn Leo, Sprague Kyle +2
A significant challenge in maintaining real-world machine learning models is responding to the continuous and unpredictable evolution of data. Most practitioners are faced with the…
cs.LG2025★ 1 cited
Understanding the Design Principles of Link Prediction in Directed Settings
Jun Zhai, Muberra Ozmen, Thomas Markovich
Link prediction is a widely studied task in Graph Representation Learning (GRL) for modeling relational data. The early theories in GRL were based on the assumption of a symmetric…