### Rational sequences converging to left-c.e. reals of positive effective Hausdorff dimension

2021年2月　アクセプト

タイトル
Rational sequences converging to left-c.e. reals of positive effective Hausdorff dimension

Hiroyuki Imai, Masahiro Kumabe, Kenshi Miyabe, Yuki Mizusawa, Toshio Suzuki

Post-conference proceedings of the 9th International Conference on Computability Theory and Foundations of Mathematics (CTFM2019)

In our previous work, we characterized Solovay reducibility using Lipschitz condition,
and introduced quasi Solovay reducibility (qS-reducibility, for short) as a H ̈older condition counterpart.
In this paper, we investigate effective dimensions and ideals closely related to quasi Solovay reducibility by means of the rate of convergence.
We show that the qS-completeness among left-c.e. reals is equivalent to having a positive effective Hausdorff dimension.
The Solovay degrees of qS-complete left-c.e. reals form a filter. On the other hand, the Solovay degrees of non-qS-complete left-c.e. reals do not form an ideal.
Based on observations on the relationships between rational sequences and reducibility, we introduce a stronger version of qS-reducibility.
Given a degree of this reducibility, the lower cone (including the given degree) forms an ideal.
By developing these investigations, we characterize the effective dimensions by means of the rate of convergence.
We give a variation of the first incompleteness theorem based on Solovay reducibility.

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### 計算可能な予測の収束速度

2020年9月16日　スライドアップロード

タイトル

ドイツ数学会　ミニシンポジウム”The impact of randomness on computation”

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DMV

### 逆ランダム

2019年11月4日　スライドアップロード

タイトル

2019年11月8日　京都大学数理情報論分野談話会

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kyoto

### 計算可能な予測

2019年7月　校正

タイトル
Computable prediction

Miyabe K. (2019) Computable Prediction. In: Hammer P., Agrawal P., Goertzel B., Iklé M. (eds) Artificial General Intelligence. AGI 2019. Lecture Notes in Computer Science, vol 11654. Springer, Cham
DOI https://doi.org/10.1007/978-3-030-27005-6_14

Abstract
We try to predict the next bit from a given finite binary string
when the sequence is sampled from a computable probability measure on the Cantor space.
There exists the best betting strategy among a class of effective ones up to a multiplicative constant,
the induced prediction from which is called algorithmic probability or universal induction by Solomonoff.
The prediction converges to the true induced measure for sufficiently random sequences.
However, the prediction is not computable.

We propose a framework to study the properties of computable predictions.
We prove that all sufficiently general computable predictions also converge to the true induced measure.
The class of sequences along which the prediction converges is related to computable randomness.

We also discuss the speed of the convergence.
We prove that, even when a computable prediction predicts a computable sequence,
the speed of the convergence cannot be bounded by a computable function monotonically decreasing to $0$.

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### 一様相対化

2019年6月19日　オンライン

タイトル
Uniform relativization

In: Manea F., Martin B., Paulusma D., Primiero G. (eds) Computing with Foresight and Industry. CiE 2019. Lecture Notes in Computer Science, vol 11558. Springer, Cham
DOI: https://doi.org/10.1007/978-3-030-22996-2_5

Abstract
This paper is a tutorial on uniform relativization. The usual relativization considers computation using an oracle, and the computation may not work for other oracles, which is similar to Turing reduction. The uniform relativization also considers computation using oracles, however, the computation should work for all oracles, which is similar to truth-table reduction. The distinction between these relativizations is important when we relativize randomness notions in algorithmic randomness, especially Schnorr randomness. For Martin-Löf randomness, its usual relativization and uniform relativization are the same so we do not need to care about this uniform relativization.

We focus on two specific examples of uniform relativization: van Lambalgen’s theorem and lowness. Van Lambalgen’s theorem holds for Schnorr randomness with the uniform relativization, but not with the usual relativization. Schnorr triviality is equivalent to lowness for Schnorr randomness with the uniform relativization, but not with the usual relativization. We also discuss some related known results.

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