Jing Jiang

Affiliations

Singapore Management University

papers

688

total citations

papers (15)

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

NEURIPS 2022arXiv

254

citations

Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast

ICML 2024arXiv

103

citations

Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

NEURIPS 2023arXiv

citations

Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates

ICLR 2025arXiv

citations

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach

AAAI 2025arXiv

citations

Cooperative Heterogeneous Deep Reinforcement Learning

NEURIPS 2020arXiv

citations

Federated Foundation Models on Heterogeneous Time Series

AAAI 2025arXiv

citations

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

CVPR 2025arXiv

citations

Bridge Then Begin Anew: Generating Target-Relevant Intermediate Model for Source-Free Visual Emotion Adaptation

AAAI 2025arXiv

citations

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

NEURIPS 2025

citations

Structured Federated Learning through Clustered Additive Modeling

NEURIPS 2023

citations

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

NEURIPS 2021

citations

Jing Jiang

Affiliations

papers (15)

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

Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast

Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding

Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates

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

Bridge Then Begin Anew: Generating Target-Relevant Intermediate Model for Source-Free Visual Emotion Adaptation

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

Structured Federated Learning through Clustered Additive Modeling

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

papers (15)

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

Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast

Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding

Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates

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

Bridge Then Begin Anew: Generating Target-Relevant Intermediate Model for Source-Free Visual Emotion Adaptation

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

Structured Federated Learning through Clustered Additive Modeling

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