Lin, Licong

13 publications

NeurIPS 2025 A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics Licong Lin, Song Mei

NeurIPS 2025 Improved Scaling Laws in Linear Regression via Data Reuse Licong Lin, Jingfeng Wu, Peter Bartlett

ICML 2025 Improving LLM Safety Alignment with Dual-Objective Optimization Xuandong Zhao, Will Cai, Tianneng Shi, David Huang, Licong Lin, Song Mei, Dawn Song

NeurIPS 2025 Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu

NeurIPSW 2024 Choose Your Anchor Wisely: Effective Unlearning Diffusion Models via Concept Reconditioning Jingyu Zhu, Ruiqi Zhang, Licong Lin, Song Mei

ICML 2024 Plug-in Performative Optimization Licong Lin, Tijana Zrnic

NeurIPS 2024 Scaling Laws in Linear Regression: Compute, Parameters, and Data Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee

NeurIPSW 2024 Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu

ICLR 2024 Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining Licong Lin, Yu Bai, Song Mei

NeurIPS 2023 Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference Licong Lin, Mufang Ying, Suvrojit Ghosh, Koulik Khamaru, Cun-Hui Zhang

NeurIPSW 2023 Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining Licong Lin, Yu Bai, Song Mei

NeurIPSW 2023 Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining Licong Lin, Yu Bai, Song Mei

JMLR 2021 What Causes the Test Error? Going Beyond Bias-Variance via ANOVA Licong Lin, Edgar Dobriban