Semi-Supervised Domain Adaptation with Non-Parametric Copulas

Abstract

A new framework based on the theory of copulas is proposed to address semi- supervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate cop- ula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model accross different learning domains. Impor- tantly, we introduce a novel vine copula model, which allows for this factorization in a non-parametric manner. Experimental results on regression problems with real-world data illustrate the efficacy of the proposed approach when compared to state-of-the-art techniques.

9 pages, Appearing on Advances in Neural Information Processing Systems 25

References in corpus (3)

• Frustratingly Easy Domain Adaptation
• Domain Adaptation: Learning Bounds and Algorithms
• A Kernel Method for the Two-Sample Problem

Cited by in corpus (6)

• Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas
• Few-shot Domain Adaptation by Causal Mechanism Transfer
• Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
• Adversarial Learning for Zero-shot Domain Adaptation
• Domain Adaptation on Graphs by Learning Aligned Graph Bases
• A Framework for Sample Efficient Interval Estimation with Control Variates