Non-stationary Domain Generalization: Theory and Algorithm
CoRR(2024)
Abstract
Although recent advances in machine learning have shown its success to learn
from independent and identically distributed (IID) data, it is vulnerable to
out-of-distribution (OOD) data in an open world. Domain generalization (DG)
deals with such an issue and it aims to learn a model from multiple source
domains that can be generalized to unseen target domains. Existing studies on
DG have largely focused on stationary settings with homogeneous source domains.
However, in many applications, domains may evolve along a specific direction
(e.g., time, space). Without accounting for such non-stationary patterns,
models trained with existing methods may fail to generalize on OOD data. In
this paper, we study domain generalization in non-stationary environment. We
first examine the impact of environmental non-stationarity on model performance
and establish the theoretical upper bounds for the model error at target
domains. Then, we propose a novel algorithm based on adaptive invariant
representation learning, which leverages the non-stationary pattern to train a
model that attains good performance on target domains. Experiments on both
synthetic and real data validate the proposed algorithm.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined