Publications

You can also find my articles on my Google Scholar profile.

Pre-Prints

Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights

Published in Arxiv, 2025

Recommended citation: Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights. Arxiv. Z. Liang, D. Tang, Y. Zhou, X. Zhao, M. Shi, W. Zhao, Z. Li, P. Wang, K. Schürholt, D. Borth, M. M. Bronstein, Y. You, Z. Wang, K. Wang
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REPA Works Until It Doesn’t: Early-Stopped, Holistic Alignment Supercharges Diffusion Training

Published in Arxiv, 2025

Recommended citation: REPA Works Until It Does not: Early-Stopped, Holistic Alignment Supercharges Diffusion Training. Arxiv. Z. Wang, W. Zhao, Y. Zhou, Z. Li, Z. Liang, M. Shi, X. Zhao, P. Zhou, K. Zhang, Z. Wang, K. Wang, Y. You
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DD-ranking: Rethinking the evaluation of dataset distillation

Published in Arxiv, 2025

Recommended citation: DD-ranking: Rethinking the evaluation of dataset distillation. Arxiv. Z. Li, X. Zhong, S. Khaki, Z. Liang, Y. Zhou, M. Shi, Z. Wang, X. Zhao, W. Zhao, Z. Qin, M. Wu, P. Zhou, H. Wang, D. J. Zhang, J. Liu, S. Wang, D. Liu, L. Zhang, G. Li, K. Wang, Z. Zhu, Z. Ma, J. T. Zhou, J. Lv, Y. Jin, P. Wang, K. Zhang, L. Lyu, Y. Huang, Z. Akata, Z. Deng, X. Wu, G. Cazenavette, Y. Shang, J. Cui, J. Gu, Q. Zheng, H. Ye, S. Wang, X. Wang, Y. Yan, A. Yao, M. Z. Shou, T. Chen, H. Bilen, B. Mirzasoleiman, M. Kellis, K. N. Plataniotis, Z. Wang, B. Zhao, Y. You, K. Wang
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Make Optimization Once and for All with Fine-grained Guidance

Published in Arxiv, 2025

Recommended citation: Make Optimization Once and for All with Fine-grained Guidance. Arxiv. M. Shi, R. Lin, X. Chen, Y. Zhou, Z. Ding, P. Li, T. Wang, K. Wang, Z. Wang, J. Zhang, T. Chen.
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GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning

Published in ACL Findings, 2025

Recommended citation: GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning. Arxiv. S. Zhou*, S. Wang*, Z. Yuan*, M. Shi, Y. Shang, D. Yang.
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Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training

Published in Arxiv, 2024

Recommended citation: Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training. Arxiv. M. Shi*, Y. Zhou*, R. Yu, Z. Li, Z. Liang, X. Zhao, X. Peng, T Rajpurohit, R. Vedantam, W. Zhao, K. Wang, Y. You.
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Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

Published in Arxiv, 2024

Recommended citation: Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation. Arxiv. X. Wu, J. Niu, X. Liu, M. Shi, G. Zhu, S. Tang.
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Conference Papers

A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training

Published in CVPR, 2025

Recommended citation: A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training. CVPR 2025. K. Wang*, M. Shi*, Y. Zhou*,Z. Yuan, Y. Shang, X. Peng, H. Zhang, Y. You
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Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints

Published in CVPR, 2025

Recommended citation: Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints. CVPR 2025. Y. Zhou, Y. Tian, J. Lv, M. Shi, Y. Li, Q. Ye, S. Zhang, J. Lv
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Federated CINN Clustering for Accurate Clustered Federated Learning

Published in ICASSP 2024, 2024

Recommended citation: Federated CINN Clustering for Accurate Clustered Federated Learning. ICASSP 2024. Y. Zhou, M. Shi, Y. Tian, Y. Li, Q. Ye, J. Lv
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PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning

Published in NeurIPS 2023, 2023

Recommended citation: PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NeurIPS 2023. M. Shi, Y. Zhou, K. Wang, H. Zhang, S. Huang, Q. Ye, J. Lv
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Unconstrained Feature Model and Its General Geometric Patterns in Federated Learning: Local Subspace Minority Collapse

Published in ICONIP 2023, 2023

Recommended citation: Unconstrained Feature Model and Its General Geometric Patterns in Federated Learning: Local Subspace Minority Collapse. ICONIP 2023. M. Shi, Y. Zhou, Q. Ye, J. Lv
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Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence.

Published in ICCV 2023, 2023

Recommended citation: Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence. ICCV 2023. Y. Zhou, M. Shi, Y. Li, Y. Sun, Q. Ye, J. Lv
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Mingjia Shi

Publications

Pre-Prints

Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights

REPA Works Until It Doesn’t: Early-Stopped, Holistic Alignment Supercharges Diffusion Training

DD-ranking: Rethinking the evaluation of dataset distillation

Make Optimization Once and for All with Fine-grained Guidance

GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning

Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

Conference Papers

A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training

Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints

Federated CINN Clustering for Accurate Clustered Federated Learning

PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning

Unconstrained Feature Model and Its General Geometric Patterns in Federated Learning: Local Subspace Minority Collapse

Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence.

Journal Articles

DeFTA: A Plug-and-Play Peer-to-Peer Decentralized Federated Learning Framework

DLB: a dynamic load balance strategy for distributed training of deep neural networks

FLSGD: free local SGD with parallel synchronization