An Information-Theoretic Framework for Out-of-Distribution Generalization
<|reference_start|>An Information-Theoretic Framework for Out-of-Distribution Generalization: We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that provides information-theoretic generalization bounds. Our framework interpolates freely between Integral Probability Metric (IPM) and $f$-divergence, which naturally recovers some known results (including Wasserstein- and KL-bounds), as well as yields new generalization bounds. Moreover, we show that our framework admits an optimal transport interpretation. When evaluated in two concrete examples, the proposed bounds either strictly improve upon existing bounds in some cases or recover the best among existing OOD generalization bounds.<|reference_end|>
@article{liu2024an,
    title={An Information-Theoretic Framework for Out-of-Distribution
  Generalization},
    author={Wenliang Liu, Guanding Yu, Lele Wang, and Renjie Liao},
    journal={arXiv preprint arXiv:2403.19895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19895},
    primaryClass={cs.IT cs.LG math.IT}
}