On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisation
Substantial efforts have been devoted to making deep learning objectives, representations, and architectures interpretable, with the goal of improving the safety, robustness, and generalisation of learning systems in diverse real-world applications. The recently proposed maximal coding rate reduction ($\mathrm{MCR}^{2}$) offers a promising information-theoretic framework for learning structured, discriminative representations of class-wise submanifolds and has inspired interpretable white-box architectures. However, we observe that $\mathrm{MCR}^{2}$ can completely fail under distribution shift, motivating our study of its out-of-distribution (OOD) generalisation limits. We establish two ...
| Authors | Menghui Zhou, Gaoshan Bi, Vitaveska Lanfranchi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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