3 papers
cs.CC2026
Learning, Compression, and Meta-Complexity
Halley Goldberg, Mikito Nanashima, Igor C. Oliveira
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. H…
cs.CC2024
Optimal Coding for Randomized Kolmogorov Complexity and Its Applications
Shuichi Hirahara, Zhenjian Lu, Mikito Nanashima
The coding theorem for Kolmogorov complexity states that any string sampled from a computable distribution has a description length close to its information content. A coding theor…
cs.LG2019
A Faster Algorithm Enumerating Relevant Features over Finite Fields
Mikito Nanashima
We consider the problem of enumerating relevant features hidden in other irrelevant information for multi-labeled data, which is formalized as learning juntas. A -junta function…