Learning, Compression, and Meta-Complexity
The idea that learning is closely tied to compression has long served as both a powerful principle and, at times, an informal analogy across machine learning and learning theory. Here we focus on settings in which that intuition can be made mathematically precise, surveying formal connections among learning algorithms, data compression, and computational complexity theory, with an emphasis on recent progress and current frontiers. We describe how techniques from complexity theory can guide the design of both supervised and unsupervised learning algorithms with provable guarantees, and conversely, how learning algorithms can yield new insights and results in complexity theory. A central th...
| Authors | Halley Goldberg, Mikito Nanashima, Igor C. Oliveira |
|---|---|
| Primary category | cs.CC |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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