18 citations · 26 across the 12 of their papers we have counts for
5 papers · 1 filter
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models
Michael Celentano, Zhou Fan, Licong Lin +1
We study mean-field variational inference in a Bayesian linear model when the sample size n is comparable to the dimension p. In high dimensions, the common approach of minimizing…
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference
Licong Lin, Mufang Ying, Suvrojit Ghosh +2
Estimation and inference in statistics pose significant challenges when data are collected adaptively. Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to…
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
Licong Lin, Yu Bai, Song Mei
Large transformer models pretrained on offline reinforcement learning datasets have demonstrated remarkable in-context reinforcement learning (ICRL) capabilities, where they can ma…
Plug-in Performative Optimization
Licong Lin, Tijana Zrnic
When predictions are performative, the choice of which predictor to deploy influences the distribution of future observations. The overarching goal in learning under performativity…
Semi-parametric inference based on adaptively collected data
Licong Lin, Koulik Khamaru, Martin J. Wainwright
Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals. We address thi…