Sparsity and Out-of-Distribution Generalization
Explaining out-of-distribution generalization has been a central problem in epistemology since Goodman's "grue" puzzle in 1946. Today it's a central problem in machine learning, including AI alignment. Here we propose a principled account of OOD generalization with three main ingredients. First, the world is always presented to experience not as an amorphous mass, but via distinguished features (for example, visual and auditory channels). Second, Occam's Razor favors hypotheses that are "sparse," meaning that they depend on as few features as possible. Third, sparse hypotheses will generalize from a training to a test distribution, provided the two distributions sufficiently overlap on th...
| Authors | Scott Aaronson, Lin Lin Lee, Jiawei Li |
|---|---|
| Primary category | cs.LG |
| Published | 8 March 2026 |
| Last updated at source | 12 May 2026 |
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