375 citations · 2.2k across the 69 of their papers we have counts for
69 papers
PPOM: Marginalizing Patch-Grid Phase for CLIP-Based Generalizable Vision-Language Prompt Tuning
Liang Wang, Haoyang Li, Chao Wang +3
Prompt tuning adapts CLIP-based vision-language models with few trainable parameters, yet its predictions remain sensitive to the spatial sampling imposed by a frozen vision transf…
Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
Shengchao Chen, Guodong Long, Dikai Liu +1
Heterogeneity in time series data is more pronounced than in vision or language, as temporal dynamics vary substantially across domains and tasks. Existing efforts on training time…
FeDaL: Federated Dataset Learning for General Time Series Foundation Models
Shengchao Chen, Guodong Long, Michael Blumenstein +1
Dataset-level heterogeneity introduces significant domain biases that fundamentally degrade generalization on general Time Series Foundation Models (TSFMs), yet this challenge rema…
Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +4
For CLIP-based prompt tuning, introducing more data as additional knowledge for enhancing fine-tuning process is proved to be an effective approach. Existing data amplification str…
FedMerge: Federated Personalization via Model Merging
Shutong Chen, Tianyi Zhou, Guodong Long +2
One global model in federated learning (FL) might not be sufficient to serve many clients with non-IID tasks and distributions. While there has been advances in FL to train multipl…
DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +3
The Base-New Trade-off (BNT) problem universally exists during the optimization of CLIP-based prompt tuning, where continuous fine-tuning on base (target) classes leads to a simult…
Federated Foundation Models on Heterogeneous Time Series
Shengchao Chen, Guodong Long, Jing Jiang +1
Training a general-purpose time series foundation models with robust generalization capabilities across diverse applications from scratch is still an open challenge. Efforts are pr…
Influence-Oriented Personalized Federated Learning
Yue Tan, Guodong Long, Jing Jiang +1
Federated learning (FL) is a machine learning paradigm where clients with different behaviors and preferences can learn collaboratively without compromising data privacy. Typical F…
Federated Vision-Language-Recommendation with Personalized Fusion
Zhiwei Li, Guodong Long, Jing Jiang +2
Applying large pre-trained Vision-Language Models to recommendation is a burgeoning field, a direction we term Vision-Language-Recommendation (VLR). Bringing VLR to user-oriented o…
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Zhiwei Li, Guodong Long, Tianyi Zhou +2
Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy in a federated setting. Existing FedCF m…
Personalized Interpretation on Federated Learning: A Virtual Concepts approach
Peng Yan, Guodong Long, Jing Jiang +1
Tackling non-IID data is an open challenge in federated learning research. Existing FL methods, including robust FL and personalized FL, are designed to improve model performance w…
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
Shengchao Chen, Guodong Long, Jing Jiang +1
This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling. We present LM-Weather, a generic appro…
Personalized Additive Modeling for Multi-level Federated Learning
Shutong Chen, Guodong Long, Tianyi Zhou +3
Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplified by highly heterogeneous clie…
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…
Dual-Personalizing Adapter for Federated Foundation Models
Yiyuan Yang, Guodong Long, Tao Shen +2
Recently, foundation models, particularly large language models (LLMs), have demonstrated an impressive ability to adapt to various tasks by fine-tuning diverse instruction data. N…
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…
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution
Shuang Ao, Tianyi Zhou, Guodong Long +2
Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes. In contrast, current reinforcement learn…
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…
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 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…