May 07, 2024
The presence of distribution shifts poses a significant challenge for deploying modern machine learning models in real-world applications. This work focuses on the target shift problem in a regression setting (Zhang et al., 2013; Nguyen et al., 2016). More specifically, the target variable y (also known as the response variable), which is continuous, has different marginal distributions in the training source and testing domain, while the conditional distribution of features x given y remains the same. While most literature focuses on classification tasks with finite target space, the regression problem has an infinite dimensional target space, which makes many of the existing methods inapplicable. In this work, we show that the continuous target shift problem can be addressed by estimating the importance weight function from an ill-posed integral equation. We propose a nonparametric regularized approach named ReTaSA to solve the ill-posed integral equation and provide theoretical justification for the estimated importance weight function. The effectiveness of the proposed method has been demonstrated with extensive numerical studies on synthetic and real-world datasets.
Written by
Hwanwoo Kim
Xin Zhang
Jiwei Zhao
Qinglong Tian
Publisher
ICLR
Research Topics
Core Machine Learning
July 21, 2024
Ouail Kitouni, Niklas Nolte, Samuel Pérez Díaz, Sokratis Trifinopoulos, Mike Williams
July 21, 2024
July 08, 2024
Antonio Orvieto, Lin Xiao
July 08, 2024
June 17, 2024
Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, Yaron Lipman
June 17, 2024
June 17, 2024
Neta Shaul, Uriel Singer, Ricky Chen, Matt Le, Ali Thabet, Albert Pumarola, Yaron Lipman
June 17, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models