Second-order Information in First-order Optimization Methods. - Yuzheng Hu; Licong Lin; Shange Tang (CoRR, 2019)

What Causes the Test Error? Going Beyond Bias-Variance via ANOVA. - Licong Lin; Edgar Dobriban (J. Mach. Learn. Res., 2021)

Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control. - Taejoo Ahn; Licong Lin; Song Mei (CoRR, 2022)

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

Semi-parametric inference based on adaptively collected data. - Licong Lin; Koulik Khamaru; Martin J. Wainwright (CoRR, 2023)

Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models. - Michael Celentano; Zhou Fan; Licong Lin; Song Mei (CoRR, 2023)

Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. - Licong Lin; Yu Bai 017; Song Mei (ICLR, 2024)

Plug-in Performative Optimization. - Licong Lin; Tijana Zrnic (ICML, 2024)

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

Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning. - Ruiqi Zhang; Licong Lin; Yu Bai 017; Song Mei (CoRR, 2024)

Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning. - Chongyu Fan; Jiancheng Liu; Licong Lin; Jinghan Jia; Ruiqi Zhang; Song Mei; Sijia Liu 001 (CoRR, 2024)

A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI. - Kazusato Oko; Licong Lin; Yuhang Cai; Song Mei (CoRR, 2025)

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

A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics. - Licong Lin; Song Mei (CoRR, 2025)

Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes. - Ruiqi Zhang; Jingfeng Wu; Licong Lin; Peter L. Bartlett (CoRR, 2025)

Improved Scaling Laws in Linear Regression via Data Reuse. - Licong Lin; Jingfeng Wu; Peter L. Bartlett (CoRR, 2025)