Guodong Long

papers

359

total citations

papers (11)

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

NEURIPS 2022arXiv

254

citations

Fine-Grained Distillation for Long Document Retrieval

AAAI 2024arXiv

citations

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach

AAAI 2025arXiv

citations

Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach

AAAI 2025arXiv

citations

WALL-E: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

NEURIPS 2025

citations

CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum

NEURIPS 2021

citations

Structured Federated Learning through Clustered Additive Modeling

NEURIPS 2023

citations

Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning

NEURIPS 2023

citations

Guodong Long

papers (11)

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Fine-Grained Distillation for Long Document Retrieval

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach

Cooperative Heterogeneous Deep Reinforcement Learning

Federated Foundation Models on Heterogeneous Time Series

DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models

Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach

WALL-E: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum

Structured Federated Learning through Clustered Additive Modeling

Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning

papers (11)

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Fine-Grained Distillation for Long Document Retrieval

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach

Cooperative Heterogeneous Deep Reinforcement Learning

Federated Foundation Models on Heterogeneous Time Series

DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models

Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach

WALL-E: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum

Structured Federated Learning through Clustered Additive Modeling

Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning