10.1k citations · 12.9k across the 89 of their papers we have counts for
89 papers
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…
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…
Re-Reading Improves Reasoning in Large Language Models
Xiaohan Xu, Chongyang Tao, Tao Shen +5
To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, i.e., \textbf{Re}-\text…
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…
One-Shot Pruning for Fast-adapting Pre-trained Models on Devices
Haiyan Zhao, Guodong Long
Large-scale pre-trained models have been remarkably successful in resolving downstream tasks. Nonetheless, deploying these models on low-capability devices still requires an effect…
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…
Improving the Robustness of Summarization Systems with Dual Augmentation
Xiuying Chen, Guodong Long, Chongyang Tao +4
A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. In this work, we first explore 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…
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
Chunxu Zhang, Guodong Long, Tianyi Zhou +3
Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recom…
Synergistic Interplay between Search and Large Language Models for Information Retrieval
Jiazhan Feng, Chongyang Tao, Xiubo Geng +5
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to mode…
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…
Large Language Models are Strong Zero-Shot Retriever
Tao Shen, Guodong Long, Xiubo Geng +3
In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retri…
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…
Style-Aware Contrastive Learning for Multi-Style Image Captioning
Yucheng Zhou, Guodong Long
Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style. However, existing methods ov…
Improving Cross-modal Alignment for Text-Guided Image Inpainting
Yucheng Zhou, Guodong Long
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-moda…
Multimodal Event Transformer for Image-guided Story Ending Generation
Yucheng Zhou, Guodong Long
Image-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but ove…
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…
Dual Personalization on Federated Recommendation
Chunxu Zhang, Guodong Long, Tianyi Zhou +4
Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used t…
Fine-Grained Distillation for Long Document Retrieval
Yucheng Zhou, Tao Shen, Xiubo Geng +4
Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a…
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…