🏢 Chinese University of Hong Kong, Shenzhen

GRDL: a novel graph classification method boasting 10x speed improvement over competitors, achieved by treating node embeddings as distributions and avoiding global pooling.

DISC, a novel control policy, disentangles untrusted ML predictions to achieve near-optimal performance when accurate, while guaranteeing competitive ratio bounds even with significant prediction erro…

Boosting graph neural networks: Topology-Invariant Pooling (TIP) leverages persistent homology to enhance graph pooling, achieving consistent performance gains across diverse datasets.

BAdam: A memory-efficient optimization method enabling full parameter fine-tuning of large language models using a block coordinate descent framework with Adam’s update rule, achieving comparable or s…

B-ary Tree Push-Pull (BTPP) achieves linear speedup for distributed learning on heterogeneous data, significantly outperforming state-of-the-art methods with minimal communication.