# Robust Classification under Class-Dependent Domain Shift

arXiv: https://arxiv.org/abs/2007.05335 · HTML: https://mlsanity.com/paper/2007.05335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Failure Modes of Domain Generalization Algorithms](https://mlsanity.com/paper/2111.13733/) (2021)

## Abstract (arXiv)

Investigation of machine learning algorithms robust to changes between the training and test distributions is an active area of research. In this paper we explore a special type of dataset shift which we call class-dependent domain shift. It is characterized by the following features: the input data causally depends on the label, the shift in the data is fully explained by a known variable, the variable which controls the shift can depend on the label, there is no shift in the label distribution. We define a simple optimization problem with an information theoretic constraint and attempt to solve it with neural networks. Experiments on a toy dataset demonstrate the proposed method is able to learn robust classifiers which generalize well to unseen domains.