192 citations · 679 across the 35 of their papers we have counts for
55 papers
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Ziqi Gao, Yifan Niu, Jiashun Cheng +6
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation…
Vertical Federated Linear Contextual Bandits
Zeyu Cao, Zhipeng Liang, Shu Zhang +5
In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i.e., contextual information is vertically distributed over differen…
Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation
Chengqian Gao, Ke Xu, Liu Liu +3
A promising paradigm for offline reinforcement learning (RL) is to constrain the learned policy to stay close to the dataset behaviors, known as policy constraint offline RL. Howev…
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
Lanqing Li, Liang Zeng, Ziqi Gao +11
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD). However, real-world pharmaceutical datasets…
Pareto-aware Neural Architecture Generation for Diverse Computational Budgets
Yong Guo, Yaofo Chen, Yin Zheng +5
Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world…
MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction
Jiahan Liu, Chaochao Yan, Yang Yu +4
Retrosynthesis is a major task for drug discovery. It is formulated as a graph-generating problem by many existing approaches. Specifically, these methods firstly identify the reac…