4 citations · 8 across the 12 of their papers we have counts for
13 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…
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…
GraphBench: Next-generation graph learning benchmarking
Timo Stoll, Chendi Qian, Ben Finkelshtein +16
Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often r…
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…
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
Qian Chen, Lei Li, Qian Li +6
A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emer…
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…
Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis
Wenxuan Ye, Chendi Qian, Xueli An +2
Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI…