10.1k citations · 12.9k across the 89 of their papers we have counts for
39 papers · 1 filter
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
Zhihong Deng, Jing Jiang, Guodong Long +1
In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maxi…
Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey
Shengchao Chen, Guodong Long, Jing Jiang +2
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric sciences is increasingly adopting data-driven models, powered by progressive develop…
Personalization Disentanglement for Federated Learning: An explainable perspective
Peng Yan, Guodong Long
Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client. This…
Causal Reinforcement Learning: A Survey
Zhihong Deng, Jing Jiang, Guodong Long +1
Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty. Despite many remarkable achievements in recent decades, applying reinfor…
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
Yijun Yang, Tianyi Zhou, Jing Jiang +2
How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a natural human skill yet challenging to achieve by current reinforcement learning: the…
Federated Prompt Learning for Weather Foundation Models on Devices
Shengchao Chen, Guodong Long, Tao Shen +2
On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing, holds significance for supporting hu…
Does Continual Learning Equally Forget All Parameters?
Haiyan Zhao, Tianyi Zhou, Guodong Long +2
Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of neural networks. Although it can be alleviated by repeatedl…
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Kun Yi, Qi Zhang, Wei Fan +7
Recently, frequency transformation (FT) has been increasingly incorporated into deep learning models to significantly enhance state-of-the-art accuracy and efficiency in time serie…
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
Haiyan Zhao, Tianyi Zhou, Guodong Long +2
As a few large-scale pre-trained models become the major choices of various applications, new challenges arise for model pruning, e.g., can we avoid pruning the same model from scr…
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data
Shengchao Chen, Guodong Long, Tao Shen +1
To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data. Despite urge…
Federated Recommendation with Additive Personalization
Zhiwei Li, Guodong Long, Tianyi Zhou
Building recommendation systems via federated learning (FL) is a new emerging challenge for advancing next-generation Internet service and privacy protection. Existing approaches t…
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Yue Tan, Yixin Liu, Guodong Long +3
Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing to the advantages of federated learning, federated graph learning (FGL) enables clients to t…
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Zhuowei Wang, Tianyi Zhou, Guodong Long +2
Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are…
Personalized Federated Learning With Graph
Fengwen Chen, Guodong Long, Zonghan Wu +2
Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new…
On the Convergence of Clustered Federated Learning
Jie Ma, Guodong Long, Tianyi Zhou +2
Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1)…
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…
TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
Zonghan Wu, Da Zheng, Shirui Pan +3
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-t…
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…
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Shaoxiong Ji, Yue Tan, Teemu Saravirta +6
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, f…
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
Zhuowei Wang, Jing Jiang, Bo Han +4
Deep learning with noisy labels is a challenging task. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL…
Cooperative Heterogeneous Deep Reinforcement Learning
Han Zheng, Pengfei Wei, Jing Jiang +3
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws. In this work, we present a Cooperative Heterogeneous Deep Reinforcemen…
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Xueping Peng, Guodong Long, Tao Shen +3
Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems. A patient's EHR data is organized as a three-level hierarchy from top…