Search Results for author:
Found 4 papers, 1 papers with code
Deep into The Domain Shift: Transfer Learning through Dependence Regularization
This paper proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals.
Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information
In this paper, we propose a moderately-balanced representation learning (MBRL) framework based on recent covariates balanced representation learning methods and orthogonal machine learning theory.
Robust Causal Learning for the Estimation of Average Treatment Effects
Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue.
Understanding Distributional Ambiguity via Non-robust Chance Constraint
Without the boundedness constraint, the CCO problem is shown to perform uniformly better than the DRO problem, irrespective of the radius of the ambiguity set, the choice of the divergence measure, or the tail heaviness of the center distribution.
