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cs.LG2025
Branching Strategies Based on Subgraph GNNs: A Study on Theoretical Promise versus Practical Reality
Junru Zhou, Yicheng Wang, Pan Li
Graph Neural Networks (GNNs) have emerged as a promising approach for ``learning to branch'' in Mixed-Integer Linear Programming (MILP). While standard Message-Passing GNNs (MPNNs)…
cs.LG2025
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry
Chi-Ning Chou, Hang Le, Yichen Wang +1
Integrating task-relevant information into neural representations is a fundamental ability of both biological and artificial intelligence systems. Recent theories have categorized…