4 citations · 8 across the 12 of their papers we have counts for
5 papers · 1 filter
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
Hao Chen, Chendi Qian, Christopher Morris +2
Exact solution of hard combinatorial optimization problems often relies on strong convex relaxations, but solving these relaxations repeatedly inside a branch-and-bound algorithm c…
On the Expressive Power of GNNs to Solve Linear SDPs
Chendi Qian, Christopher Morris
Semidefinite programs (SDPs) are a powerful framework for convex optimization and for constructing strong relaxations of hard combinatorial problems. However, solving large SDPs ca…
Topology-Aware Revival for Efficient Sparse Training
Meiling Jin, Fei Wang, Xiaoyun Yuan +2
Static sparse training is a promising route to efficient learning by committing to a fixed mask pattern, yet the constrained structure reduces robustness. Early pruning decisions c…
Learning to Approximate Uniform Facility Location via Graph Neural Networks
Chendi Qian, Christopher Morris, Stefanie Jegelka +1
Neural networks, particularly message-passing neural networks (MPNNs), are increasingly used as heuristics for hard combinatorial optimization problems. Yet many learning-based met…
GraIP: A Benchmarking Framework For Neural Graph Inverse Problems
Semih Cantürk, Andrei Manolache, Arman Mielke +5
A wide range of graph learning tasks, such as structure discovery, temporal graph analysis, and combinatorial optimization, focus on inferring graph structures from data, rather th…