4 citations · 7 across the 8 of their papers we have counts for
9 papers
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…
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…
Principled Data Augmentation for Learning to Solve Quadratic Programming Problems
Chendi Qian, Christopher Morris
Linear and quadratic optimization are crucial in numerous real-world applications, ranging from training machine learning models to solving integer linear programs. Recently, learn…
Towards graph neural networks for provably solving convex optimization problems
Chendi Qian, Christopher Morris
Recently, message-passing graph neural networks (MPNNs) have shown potential for solving combinatorial and continuous optimization problems due to their ability to capture variable…
Probabilistic Graph Rewiring via Virtual Nodes
Chendi Qian, Andrei Manolache, Christopher Morris +1
Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning. Despite their effectiveness, MPNNs face challenges such as under…
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
Chendi Qian, Didier Chételat, Christopher Morris
Recently, machine learning, particularly message-passing graph neural networks (MPNNs), has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed up…
Probabilistically Rewired Message-Passing Neural Networks
Chendi Qian, Andrei Manolache, Kareem Ahmed +4
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring pote…