2 Search Results for "Chernov, Alexey"


Document
Plain Stopping Time and Conditional Complexities Revisited

Authors: Mikhail Andreev, Gleb Posobin, and Alexander Shen

Published in: LIPIcs, Volume 117, 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018)


Abstract
In this paper we analyze the notion of "stopping time complexity", the amount of information needed to specify when to stop while reading an infinite sequence. This notion was introduced by Vovk and Pavlovic [Vovk and Pavlovic, 2016]. It turns out that plain stopping time complexity of a binary string x could be equivalently defined as (a) the minimal plain complexity of a Turing machine that stops after reading x on a one-directional input tape; (b) the minimal plain complexity of an algorithm that enumerates a prefix-free set containing x; (c) the conditional complexity C(x|x*) where x in the condition is understood as a prefix of an infinite binary sequence while the first x is understood as a terminated binary string; (d) as a minimal upper semicomputable function K such that each binary sequence has at most 2^n prefixes z such that K(z)<n; (e) as maxC^X(x) where C^X(z) is plain Kolmogorov complexity of z relative to oracle X and the maximum is taken over all extensions X of x. We also show that some of these equivalent definitions become non-equivalent in the more general setting where the condition y and the object x may differ, and answer an open question from Chernov, Hutter and Schmidhuber [Alexey V. Chernov et al., 2007].

Cite as

Mikhail Andreev, Gleb Posobin, and Alexander Shen. Plain Stopping Time and Conditional Complexities Revisited. In 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 117, pp. 2:1-2:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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@InProceedings{andreev_et_al:LIPIcs.MFCS.2018.2,
  author =	{Andreev, Mikhail and Posobin, Gleb and Shen, Alexander},
  title =	{{Plain Stopping Time and Conditional Complexities Revisited}},
  booktitle =	{43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018)},
  pages =	{2:1--2:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-086-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{117},
  editor =	{Potapov, Igor and Spirakis, Paul and Worrell, James},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2018.2},
  URN =		{urn:nbn:de:0030-drops-95842},
  doi =		{10.4230/LIPIcs.MFCS.2018.2},
  annote =	{Keywords: Kolmogorov complexity, stopping time complexity, structured conditional complexity, algorithmic information theory}
}
Document
Complexity Monotone in Conditions and Future Prediction Errors

Authors: Alexey Chernov, Marcus Hutter, and Jürgen Schmidhuber

Published in: Dagstuhl Seminar Proceedings, Volume 6051, Kolmogorov Complexity and Applications (2006)


Abstract
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor $M$ from the true distribution $mu$ by the algorithmic complexity of $mu$. Here we assume we are at a time $t>1$ and already observed $x=x_1...x_t$. We bound the future prediction performance on $x_{t+1}x_{t+2}...$ by a new variant of algorithmic complexity of $mu$ given $x$, plus the complexity of the randomness deficiency of $x$. The new complexity is monotone in its condition in the sense that this complexity can only decrease if the condition is prolonged. We also briefly discuss potential generalizations to Bayesian model classes and to classification problems.

Cite as

Alexey Chernov, Marcus Hutter, and Jürgen Schmidhuber. Complexity Monotone in Conditions and Future Prediction Errors. In Kolmogorov Complexity and Applications. Dagstuhl Seminar Proceedings, Volume 6051, pp. 1-20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)


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@InProceedings{chernov_et_al:DagSemProc.06051.6,
  author =	{Chernov, Alexey and Hutter, Marcus and Schmidhuber, J\"{u}rgen},
  title =	{{Complexity Monotone in Conditions and Future Prediction Errors}},
  booktitle =	{Kolmogorov Complexity and Applications},
  pages =	{1--20},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6051},
  editor =	{Marcus Hutter and Wolfgang Merkle and Paul M.B. Vitanyi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.06051.6},
  URN =		{urn:nbn:de:0030-drops-6327},
  doi =		{10.4230/DagSemProc.06051.6},
  annote =	{Keywords: Kolmogorov complexity, posterior bounds, online sequential prediction, Solomonoff prior, monotone conditional complexity, total error, future loss, ra}
}
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