| 2026 | KDD | PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers. | Yibo Zhong, Haoxiang Jiang, Lincan Li, Ryumei Nakada, Tianci Liu, Linjun Zhang, Huaxiu Yao, Haoyu Wang |
| 2025 | ACL | RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization. | Tianci Liu, Haoxiang Jiang, Tianze Wang, Ran Xu, Yue Yu, Linjun Zhang, Tuo Zhao, Haoyu Wang |
| 2025 | ICLR | MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models. | Peng Xia, Kangyu Zhu, Haoran Li, Tianze Wang, Weijia Shi, Sheng Wang, Linjun Zhang, James Zou, Huaxiu Yao |
| 2025 | ICML | Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing. | Tianci Liu, Ruirui Li, Zihan Dong, Hui Liu, Xianfeng Tang, Qingyu Yin, Linjun Zhang, Haoyu Wang, Jing Gao |
| 2025 | ICML | FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees. | Fan Nie, Xiaotian Hou, Shuhang Lin, James Zou, Huaxiu Yao, Linjun Zhang |
| 2025 | ICML | MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment. | Tianze Wang, Dongnan Gui, Yifan Hu, Shuhang Lin, Linjun Zhang |
| 2025 | TCC | Differentially Private Learning Beyond the Classical Dimensionality Regime. | Cynthia Dwork, Pranay Tankala, Linjun Zhang |
| 2024 | EMNLP | RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models. | Peng Xia, Kangyu Zhu, Haoran Li, Hongtu Zhu, Yun Li, Gang Li, Linjun Zhang, Huaxiu Yao |
| 2024 | ICLR | Analyzing and Mitigating Object Hallucination in Large Vision-Language Models. | Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao |
| 2024 | ICML | Conformal Prediction for Deep Classifier via Label Ranking. | Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, Hongxin Wei |
| 2024 | ICML | Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. | Lujing Zhang, Aaron Roth, Linjun Zhang |
| 2024 | WACV | Peak Period Demand Forecasting with Proxy Data: GNN-Enhanced Meta-Learning. | Zexing Xu, Linjun Zhang, Sitan Yang, Nan Jiang |
| 2023 | AISTATS | Reinforcement Learning with Stepwise Fairness Constraints. | Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David C. Parkes |
| 2023 | AISTATS | Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. | Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang |
| 2023 | AISTATS | Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise. | Haotian Ye, James Zou, Linjun Zhang |
| 2023 | ICLR | FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. | Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, James Zou |
| 2023 | ICLR | FaiREE: fair classification with finite-sample and distribution-free guarantee. | Puheng Li, James Zou, Linjun Zhang |
| 2023 | ICML | Discover and Cure: Concept-aware Mitigation of Spurious Correlation. | Shirley Wu, Mert Yksekgnl, Linjun Zhang, James Zou |
| 2022 | ICLR | Meta-Learning with Fewer Tasks through Task Interpolation. | Huaxiu Yao, Linjun Zhang, Chelsea Finn |
| 2022 | ICML | Improving Out-of-Distribution Robustness via Selective Augmentation. | Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn |
| 2022 | ICML | When and How Mixup Improves Calibration. | Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou |
| 2021 | AISTATS | Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. | Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Zou |
| 2021 | ICLR | How Does Mixup Help With Robustness and Generalization? | Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou |
| 2021 | ICML | Improving Generalization in Meta-learning via Task Augmentation. | Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, Zhenhui Li |
| 2020 | ICML | Interpreting Robust Optimization via Adversarial Influence Functions. | Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang |
| 2014 | ISCAS | How is that complex network complex? | Michael Small, Kevin Judd, Linjun Zhang |