4 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
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…