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stat.ML2024
In-context Learning for Mixture of Linear Regressions: Existence, Generalization and Training Dynamics
Yanhao Jin, Krishnakumar Balasubramanian, Lifeng Lai
We investigate the in-context learning capabilities of transformers for the -dimensional mixture of linear regression model, providing theoretical insights into their existence,…
stat.ML2024
Transformers Handle Endogeneity in In-Context Linear Regression
Haodong Liang, Krishnakumar Balasubramanian, Lifeng Lai
We explore the capability of transformers to address endogeneity in in-context linear regression. Our main finding is that transformers inherently possess a mechanism to handle end…
math.ST2024
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation
Yanhao Jin, Krishnakumar Balasubramanian, Debashis Paul
Meta-learning involves training models on a variety of training tasks in a way that enables them to generalize well on new, unseen test tasks. In this work, we consider meta-learni…