Lin, Yong

18 publications

ICLR 2026 Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui-Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, Jiayun Wu, Jiri Gesi, Ximing Lu, David Acuna, Kaiyu Yang, Hongzhou Lin, Yejin Choi, Danqi Chen, Sanjeev Arora, Chi Jin

TMLR 2026 Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Wenzhe Li, Yong Lin, Mengzhou Xia, Chi Jin

ICLR 2026 STAT: Skill-Targeted Adaptive Training Yinghui He, Abhishek Panigrahi, Yong Lin, Sanjeev Arora

ICLR 2026 Why Is Your Language Model a Poor Implicit Reward Model? Noam Razin, Yong Lin, Jiarui Yao, Sanjeev Arora

TMLR 2025 Entropy-Regularized Process Reward Model Hanning Zhang, Pengcheng Wang, Shizhe Diao, Yong Lin, Rui Pan, Hanze Dong, Dylan Zhang, Pavlo Molchanov, Tong Zhang

NeurIPS 2025 Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving of Inequalities Haoyu Zhao, Yihan Geng, Shange Tang, Yong Lin, Bohan Lyu, Hongzhou Lin, Chi Jin, Sanjeev Arora

TMLR 2025 Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic Yifei He, Yuzheng Hu, Yong Lin, Tong Zhang, Han Zhao

JMLR 2025 Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, Tong Zhang

NeurIPS 2024 A Sober Look at the Robustness of CLIPs to Spurious Features Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang

NeurIPS 2024 Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs Rui Yang, Ruomeng Ding, Yong Lin, Huan Zhang, Tong Zhang

NeurIPSW 2024 Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Wenzhe Li, Yong Lin, Mengzhou Xia, Chi Jin

NeurIPS 2023 ID and OOD Performance Are Sometimes Inversely Correlated on Real-World Datasets Damien Teney, Yong Lin, Seong Joon Oh, Ehsan Abbasnejad

AAAI 2023 Stable Learning via Sparse Variable Independence Han Yu, Peng Cui, Yue He, Zheyan Shen, Yong Lin, Renzhe Xu, Xingxuan Zhang

CVPR 2022 Bayesian Invariant Risk Minimization Yong Lin, Hanze Dong, Hao Wang, Tong Zhang

ICML 2022 Model Agnostic Sample Reweighting for Out-of-Distribution Learning Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang

ICML 2022 Probabilistic Bilevel Coreset Selection Xiao Zhou, Renjie Pi, Weizhong Zhang, Yong Lin, Zonghao Chen, Tong Zhang

ICML 2022 Sparse Invariant Risk Minimization Xiao Zhou, Yong Lin, Weizhong Zhang, Tong Zhang

NeurIPS 2022 ZIN: When and How to Learn Invariance Without Environment Partition? Yong Lin, Shengyu Zhu, Lu Tan, Peng Cui