7 papers
Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety
Seok-Jin Kim
We study the multi-task linear regression problem in the presence of contaminated tasks. We address the setting where the unknown parameters of a majority of tasks are close in the…
Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression
Seok-Jin Kim, Kaizheng Wang
We study the problem of estimating the effect function for a continuous treatment, which maps each treatment value to a population-averaged outcome. A central challenge in this set…
Nearly Optimal Best Arm Identification for Semiparametric Bandits
Seok-Jin Kim
We study fixed-confidence Best Arm Identification (BAI) in semiparametric bandits, where rewards are linear in arm features plus an unknown additive baseline shift. Unlike linear-b…
Optimal and Structure-Adaptive CATE Estimation with Kernel Ridge Regression
Seok-Jin Kim
We propose an optimal algorithm for estimating conditional average treatment effects (CATEs) when response functions lie in a reproducing kernel Hilbert space (RKHS). We study sett…
Experimental Design for Semiparametric Bandits
Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh
We study finite-armed semiparametric bandits, where each arm's reward combines a linear component with an unknown, potentially adversarial shift. This model strictly generalizes cl…
Transfer Learning of CATE with Kernel Ridge Regression
Seok-Jin Kim, Hongjie Liu, Molei Liu +1
The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outco…