3 accepted papers
We study the multi-task linear regression problem with contaminated tasks. We consider a situation where the unknown parameters of each task are close in the $\ell_2$-norm, but a certain proportion of tasks are outliers. In the presence of outliers, existing works develop theory under the assumption…
We consider the problem of estimating the effect function of a continuous treatment, which maps each treatment value to its average effect over the population. A central challenge in this setting is confounding: treatment assignment often depends on covariates, creating selection bias that makes dir…
We study the performance guarantees of exploration-free greedy algorithms for the linear contextual bandit problem. We introduce a novel condition, named the \textit{Local Anti-Concentration} (LAC) condition, which enables a greedy bandit algorithm to achieve provable efficiency. We show that the…