Publications

($^*$ denotes alphabetical ordering or co-first author)

Preprints

L. Lin, J. Wu, S. M. Kakade, P. L. Bartlett, J. D. Lee. Scaling Laws in Linear Regression: Compute, Parameters, and Data. Preprint, 2024+. arXiv

R. Zhang, L. Lin*, Y. Bai, S. Mei. Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning. Preprint, 2024+. arXiv

M. Celentano, Z. Fan, L. Lin*, and S. Mei. Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models. Preprint, 2023+. arXiv

L. Lin, K. Khamaru, M. J. Wainwright. Semi-parametric inference based on adaptively collected data. Preprint, 2023+. arXiv

T. Ahn, L. Lin*, S. Mei. Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control. Preprint, 2022+. arXiv

Publications

L. Lin, T. Zrnic. Plug-in Performative Optimization. International Conference on Machine Learning (ICML), 2024. arXiv

L. Lin, Y. Bai, S. Mei. Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. International Conference on Learning Representations (ICLR), 2024. arXiv

L. Lin, M. Ying, S. Ghosh, K. Khamaru, and C. Zhang. Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference. Advances in Neural Information Processing Systems (NeurIPS), 2023. arXiv

L. Lin, E. Dobriban. What causes the test error? going beyond bias-variance via anova. The Journal of Machine Learning Research, 2021. journal arXiv