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cs.LG2026
Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation
Mingxuan Ouyang, Hao Lan, Wanyu Lin
Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design. Current methods primarily rely on supervised training o…
cs.LG2024
Debiasing Graph Representation Learning based on Information Bottleneck
Ziyi Zhang, Mingxuan Ouyang, Wanyu Lin +2
Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make…
cs.LG2024
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck
Yuntao Shou, Haozhi Lan, Xiangyong Cao
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer…