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Miao's Group

SELECTED PUBLICATIONS

Only list papers that I am the first-author or main supervisor. Full publications can be found in My Google Scholar profile. Codes for all publications can be found in Our Group Github.

  • Haomiao Qiu, Miao Zhang#, Ziyue Qiao#, Liqiang Nie, Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning. (NeurIPS 2025, CCF A)

  • Jiarui Jiang, Wei Huang, Miao Zhang#, Taiji Suzuki, Liqiang Nie, Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression. (NeurIPS 2025, CCF A)

  • Gongwei Chen, Lirong Jie, Lexiao Zou, Weili Guan, Miao Zhang#, Liqiang Nie#, Enhancing GUI Agent with Uncertainty-Aware Self-Trained Evaluator. (NeurIPS 2025, CCF A)

  • Ming Wang, Miao Zhang#, Xuebo Liu, Liqiang Nie, Weight-Aware Activation Sparsity with Constrained Bayesian Optimization Scheduling for Large Language Models. (EMNLP 2025, Main, Oral, CCF B)

  • Jiaqi Zhao, Chao Zeng, Ming Wang, Linxuan Han, Yuzhang Shang, Miao Zhang#, Liqiang Nie, LRQuant+: A Unified and Learnable Framework to Post-training Quantization for Transformer-based Large Foundation Models. (IEEE TPAMI 2025, CCF A)

  • Jiaqi Zhao, Miao Zhang#, Ming Wang, Yuzhang Shang, Kaihao Zhang, Weili Guan, Yaowei Wang, Min Zhang, PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models. (ACL 2025, CCF A)

  • Li Wang*, Chao Zeng*, Miao Zhang#, Jianlong Wu, Liqiang Nie, Domain Aware Post Training Quantization for Vision Transformers in Deployment. (Pattern Recognition 2025, CCF B)

  • Hongyi Wan*, Shiyuan Ren*, Wei Huang#, Miao Zhang#, Xiang Deng, Yixin Bao, Liqiang Nie，Understanding the Forgetting of (Replay-based) Continual Learning via Feature Learning: Angle Matters. (ICML 2025, CCF A)

  • Lexiao Zou, Gongwei Chen, Yanda Chen,Miao Zhang#, Enhancing Diffusion-based Dataset Distillation via Adversary-Guided Curriculum Sampling. (ICME 2025, CCF B)

  • Kai Zhao*, Zhihao Zhuang*, Miao Zhang#, Chenjuan Guo, Yang Shu, Bin Yang, Enhancing Diversity for Data-free Quantization. (CVPR 2025, CCF A).

  • Yanda Chen*, Gongwei Chen*, Miao Zhang#, Weili Guan, Liqiang Nie, Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation. (CVPR 2025, CCF A).

  • Qianlong Xiang, Miao Zhang#, Yuzhang Shang, Jianlong Wu, Yan Yan, Liqiang Nie#, DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture. (CVPR 2025, CCF A).

  • Jiarui Jiang, Wei Huang, Miao Zhang#, Taiji Suzuki, Liqiang Nie, Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization. (NeurIPS 2024, CCF A). [ Paper and Code]

  • Hongrong Cheng, Miao Zhang#, Javen Qinfeng Shi, A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations. (IEEE TPAMI 2024, CCF A). [ Paper and Code]

  • Hongrong Cheng, Miao Zhang#, Javen Qinfeng Shi, Influence Function Based Second-Order Channel Pruning: Evaluating True Loss Changes For Pruning Is Possible Without Retraining. (IEEE TPAMI 2024). [ Paper and Code]

  • Jiaqi Zhao, Miao Zhang#, Chao Zeng, Ming Wang, Xuebo Liu, Liqiang Nie, LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models. (ACL 2024, Main, Oral, CCF A). [ Paper and Code]

  • Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan, Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data. (Spotlight, NeurIPS 2023, CCF A). [ Paper and Code]

  • Xin Zheng, Miao Zhang, Chunyang Chen, Soheila Molaei, Chuan Zhou, Shirui Pan, GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels. (NeurIPS 2023, CCF A). [ Paper and Code]

  • David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, and Christian S. Jensen, LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation. (SIGMOD 2023, CCF A). [ Paper and Code]

  • Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan, Auto-HeG: Automated Graph Neural Network on Heterophilic. (WWW 2023, CCF A). [ Paper and Code]

  • Xin Zheng, Miao Zhang, Chunyang Chen, Chaojie Li, Chuan Zhou, and Shirui Pan, Multi-Relational Graph Neural Architecture Search with Fine-grained Message Passing. (ICDM 2022, CORE Rank A*, CCF B).

  • Miao Zhang, Wei Huang, Bin Yang, Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations. (Accepted by NeurIPS2022, CCF A). [ Paper and Code]

  • Miao Zhang, Li Wang, David Gonzalo Chaves Campos, Wei Huang, Chenjuan Guo, Bin Yang, Weighted Mutual Learning with Diversity-Driven Model Compression. (Accepted by NeurIPS2022, CCF A). [ Paper and Code]

  • Miao Zhang, Shirui Pan, Xiaojun Chang, Steven Su, Jilin Hu, Gholamreza Haffari, Bin Yang, Differentiable Neural Architecture Search via Bayesian Learning Rule. (Accepted by CVPR2022, CCF A). [ Paper and Code]

  • Miao Zhang, Steven Su, Shirui Pan, Xiaojun Chang, Ehsan Abbasnejad, Reza Haffari, iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients. (Accepted by ICML2021, CCF A). [ Paper and Code]

  • Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Zongyuan Ge, and Steven Su, One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting. (IEEE TPAMI 2025 CCF A). [ Paper and Code]

  • Miao Zhang, Huiqi Li, Shirui Pan, Juan Lyu, Steve Ling, Steven Su, Hyperparameter Optimization with Non-stationary Kernel for CNN based Lung Nodule Classification. (IEEE Transaction on Evolutionary Computing (TEvC) ). [ Paper and Code]

  • Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Zongyuan Ge, Steven Su, Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement. (NeurIPS2020, CCF A). [ Paper and Code]

  • Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Steven Su, Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization. (CVPR2020, CCF A). [ Paper and Code]

  • Miao Zhang, Huiqi Li, Shirui Pan, Taoping Liu, Steven Su, One-Shot Neural Architecture Search via Novelty Driven Sampling. (IJCAI2020, CCF A). [ Paper and Code]

  • Miao Zhang, Huiqi Li, Steven Su, High Dimensional Bayesian Optimization via Supervised Dimension Reduction. (IJCAI2019., CCF A). [ Paper and Code]




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