419 citations · 932 across the 46 of their papers we have counts for
33 papers · 1 filter
APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems
Shentong Mo, Yatao Bian
Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery. While flow-matching models (, FlowDPO) have recently shown promise i…
Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions
Yongquan Shi, Zijing Ou, Shiping Wang +1
Learning neural set functions is pivotal to a wide range of important applications, including compound selection in AI-driven drug discovery and product recommendation. Recent work…
InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization
Yifan Niu, Ziqi Gao, Tingyang Xu +5
Exploring chemical space to find novel molecules that simultaneously satisfy multiple properties is crucial in drug discovery. However, existing methods often struggle with trading…
SAILOR: Structural Augmentation Based Tail Node Representation Learning
Jie Liao, Jintang Li, Liang Chen +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the…
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
Qitian Wu, Wentao Zhao, Chenxiao Yang +5
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class o…
Reweighted Mixup for Subpopulation Shift
Zongbo Han, Zhipeng Liang, Fan Yang +8
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different…
Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Yongqiang Chen, Wei Huang, Kaiwen Zhou +3
A common explanation for the failure of out-of-distribution (OOD) generalization is that the model trained with empirical risk minimization (ERM) learns spurious features instead o…
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…
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Zongbo Han, Zhipeng Liang, Fan Yang +7
Subpopulation shift widely exists in many real-world machine learning applications, referring to the training and test distributions containing the same subpopulation groups but va…
Diversity Boosted Learning for Domain Generalization with Large Number of Domains
Xi Leng, Xiaoying Tang, Yatao Bian
Machine learning algorithms minimizing the average training loss usually suffer from poor generalization performance due to the greedy exploitation of correlations among the traini…
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
Yongqiang Chen, Kaiwen Zhou, Yatao Bian +8
Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data. Most efforts are devoted to advancin…
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
Bingzhe Wu, Zhipeng Liang, Yuxuan Han +3
Recently, federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. Nevertheless, di…
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
Jiying Zhang, Xi Xiao, Long-Kai Huang +2
Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world…
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs
Jiying Zhang, Fuyang Li, Xi Xiao +4
As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community. However, commonly used frameworks in deep hypergraph le…
Learning Neural Set Functions Under the Optimal Subset Oracle
Zijing Ou, Tingyang Xu, Qinliang Su +3
Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority o…
Transformer for Graphs: An Overview from Architecture Perspective
Erxue Min, Runfa Chen, Yatao Bian +7
Recently, Transformer model, which has achieved great success in many artificial intelligence fields, has demonstrated its great potential in modeling graph-structured data. Till n…
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Jintang Li, Bingzhe Wu, Chengbin Hou +5
Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery. Despit…
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian +6
Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e.g., images), studies on graph data are still limited. Dif…
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
Yuanfeng Ji, Lu Zhang, Jiaxiang Wu +16
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its…
Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis
Bingzhe Wu, Zhicong Liang, Yatao Bian +3
Recently, generalization bounds of the non-convex empirical risk minimization paradigm using Stochastic Gradient Langevin Dynamics (SGLD) have been extensively studied. Several the…
-Laplacian Based Graph Neural Networks
Guoji Fu, Peilin Zhao, Yatao Bian
Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and top…
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
Yatao Bian, Yu Rong, Tingyang Xu +3
Valuation problems, such as feature interpretation, data valuation and model valuation for ensembles, become increasingly more important in many machine learning applications. Such…
Recognizing Predictive Substructures with Subgraph Information Bottleneck
Junchi Yu, Tingyang Xu, Yu Rong +3
The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack…