| 2026 | ICPR | MMFuser: Multimodal Multi-layer Feature Fuser for Fine-Grained Vision-Language Understanding. | Yue Cao, Yong Huang, Wei Zhu, Yangzhou Liu, Zhe Chen, Guangchen Shi, Yong Fa, Yujie Yang, Song Mei, Tong Lu |
| 2025 | ICLR | U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models. | Song Mei |
| 2025 | ICML | Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks. | Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett |
| 2025 | ICML | Improving LLM Safety Alignment with Dual-Objective Optimization. | Xuandong Zhao, Will Cai, Tianneng Shi, David Huang, Licong Lin, Song Mei, Dawn Song |
| 2024 | ICLR | How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations. | Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai |
| 2024 | ICLR | Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. | Licong Lin, Yu Bai, Song Mei |
| 2023 | ICLR | Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. | Fan Chen, Yu Bai, Song Mei |
| 2023 | ICML | Lower Bounds for Learning in Revealing POMDPs. | Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai |
| 2022 | FOCS | Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models. | Joao Basso, David Gamarnik, Song Mei, Leo Zhou |
| 2022 | ICLR | Efficient and Differentiable Conformal Prediction with General Function Classes. | Yu Bai, Song Mei, Huan Wang, Yingbo Zhou, Caiming Xiong |
| 2022 | ICLR | The Three Stages of Learning Dynamics in High-dimensional Kernel Methods. | Nikhil Ghosh, Song Mei, Bin Yu |
| 2022 | ICLR | When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? | Ziang Song, Song Mei, Yu Bai |
| 2022 | ICML | Near-Optimal Learning of Extensive-Form Games with Imperfect Information. | Yu Bai, Chi Jin, Song Mei, Tiancheng Yu |
| 2021 | COLT | Learning with invariances in random features and kernel models. | Song Mei, Theodor Misiakiewicz, Andrea Montanari |
| 2021 | ICML | Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. | Yu Bai, Song Mei, Huan Wang, Caiming Xiong |
| 2021 | ICML | Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. | Zitong Yang, Yu Bai, Song Mei |
| 2019 | COLT | Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit. | Song Mei, Theodor Misiakiewicz, Andrea Montanari |
| 2017 | COLT | Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality. | Song Mei, Theodor Misiakiewicz, Andrea Montanari, Roberto Imbuzeiro Oliveira |
| 2008 | CISIS | Research and Analysis of Topology Control in NS-2 for Ad-hoc Wireless Network. | Li Xu, Hui Bo, Haixia Liu, Mingqiang Yang, Song Mei, Guo Wei |