Resource bounded Ku\v{c}era-G\'{a}cs Theorems
Satyadev Nandakumar, Akhil S, Chandra Shekhar Tiwari

TL;DR
This paper explores resource-bounded versions of the Kučera-Gács Theorem, establishing polynomial-time and finite-state analogues, and analyzing their implications for randomness, compressibility, and computational reductions.
Contribution
It proves a quasi-polynomial-time Kučera-Gács Theorem, characterizes polynomial-time compressibility, and demonstrates the failure of the theorem for finite-state reductions.
Findings
Every sequence is quasi-polynomial-time reducible to a polynomial-time random sequence.
Lower oracle use for such reductions is n+o(n) bits.
The lower polynomial-time Turing decompression ratio equals polynomial-time Kolmogorov complexity.
Abstract
The Ku\v{c}era--G\'{a}cs theorem is a fundamental result in algorithmic randomness. It states that every infinite sequence is Turing reducible to a Martin-L\"of random . This paper studies resource-bounded analogues of the Ku\v{c}era-G\'acs Theorem, at the resource bounds of polynomial-time and finite-state computation. We prove a {quasi-polynomial-time}{ Ku\v{c}era-G\'acs Theorem}, showing that every infinite sequence is quasi-polynomial-time reducible to a \emph{polynomial-time random} sequence . We also show that for any , the oracle use of is bits for obtaining the first bits of . We then study the relationship between compressibility and Turing reductions, in the polynomial-time setting. We establish that , demonstrating that the lower polynomial-time Turing decompression ratio is precisely…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
