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Documents authored by Gammerman, Alex

Document
DOI: 10.4230/OASIcs.ICCSW.2013.27
Conformal Prediction under Hypergraphical Models

Authors: Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, and Vladimir Vovk

Published in: OASIcs, Volume 35, 2013 Imperial College Computing Student Workshop

Abstract
Conformal predictors are usually defined and studied under the exchangeability assumption. However, their definition can be extended to a wide class of statistical models, called online compression models, while retaining their property of automatic validity. This paper is devoted to conformal prediction under hypergraphical models that are more specific than the exchangeability model. We define conformity measures for such hypergraphical models and study the corresponding conformal predictors empirically on benchmark LED data sets. Our experiments show that they are more efficient than conformal predictors that use only the exchangeability assumption.
Cite as

Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, and Vladimir Vovk. Conformal Prediction under Hypergraphical Models. In 2013 Imperial College Computing Student Workshop. Open Access Series in Informatics (OASIcs), Volume 35, pp. 27-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{fedorova_et_al:OASIcs.ICCSW.2013.27,
  author =	{Fedorova, Valentina and Gammerman, Alex and Nouretdinov, Ilia and Vovk, Vladimir},
  title =	{{Conformal Prediction under Hypergraphical Models}},
  booktitle =	{2013 Imperial College Computing Student Workshop},
  pages =	{27--34},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-63-7},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{35},
  editor =	{Jones, Andrew V. and Ng, Nicholas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2013.27},
  URN =		{urn:nbn:de:0030-drops-42685},
  doi =		{10.4230/OASIcs.ICCSW.2013.27},
  annote =	{Keywords: conformal prediction, hypergraphical models, conformity measure}
}
Document
DOI: 10.4230/DagSemProc.06051.2
Application of Kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series.

Authors: Boris Ryabko, Jaakko Astola, and Alex Gammerman

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

Abstract
We show that Kolmogorov complexity and such its estimators as universal codes (or data compression methods) can be applied for hypothesis testing in a framework of classical mathematical statistics. The methods for identity testing and nonparametric testing of serial independence for time series are described.
Cite as

Boris Ryabko, Jaakko Astola, and Alex Gammerman. Application of Kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series.. In Kolmogorov Complexity and Applications. Dagstuhl Seminar Proceedings, Volume 6051, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{ryabko_et_al:DagSemProc.06051.2,
  author =	{Ryabko, Boris and Astola, Jaakko and Gammerman, Alex},
  title =	{{Application of Kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series.}},
  booktitle =	{Kolmogorov Complexity and Applications},
  pages =	{1--13},
  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.dagstuhl.de/entities/document/10.4230/DagSemProc.06051.2},
  URN =		{urn:nbn:de:0030-drops-6363},
  doi =		{10.4230/DagSemProc.06051.2},
  annote =	{Keywords: Algorithmic complexity, algorithmic information theory, Kolmogorov complexity, universal coding, hypothesis testing}
}
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