375 citations · 2.2k across the 69 of their papers we have counts for
11 papers · 1 filter
False Correlation Reduction for Offline Reinforcement Learning
Zhihong Deng, Zuyue Fu, Lingxiao Wang +5
Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems. Most existing papers only discuss defending against out-of-d…
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Xueping Peng, Guodong Long, Tao Shen +2
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a pati…
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Guodong Long, Tao Shen, Yue Tan +3
Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across user…
Federated Learning for Open Banking
Guodong Long, Yue Tan, Jing Jiang +1
Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial servi…
Multi-Center Federated Learning: Clients Clustering for Better Personalization
Guodong Long, Ming Xie, Tao Shen +4
Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent cl…
Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering
Zonghan Wu, Shirui Pan, Guodong Long +2
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings.…
FedProto: Federated Prototype Learning across Heterogeneous Clients
Yue Tan, Guodong Long, Lu Liu +4
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occur…
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task
Xiaohan Zhang, Lu Liu, Guodong Long +2
Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks. A key problem is t…
Isometric Propagation Network for Generalized Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +3
Zero-shot learning (ZSL) aims to classify images of an unseen class only based on a few attributes describing that class but no access to any training sample. A popular strategy is…
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack
Jie Wang, Zhaoxia Yin, Jin Tang +2
The studies on black-box adversarial attacks have become increasingly prevalent due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs). Howev…
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization
Jie Wang, Zhaoxia Yin, Jing Jiang +1
Fooling deep neural networks (DNNs) with the black-box optimization has become a popular adversarial attack fashion, as the structural prior knowledge of DNNs is always unknown. Ne…