About the Institute

Institute of Theoretical and Applied Informatics, Polish Academy of Sciences is a research institute whose science activity concentrates in the area of Information Technology. The Institute is also involved in training an advanced level technical and scientific staff and both initiates and participates in projects aimed at development of innovative commercial sector. ITAI takes part in realization of Polish Academy of Sciences' mission of advancement promotion, integration and dissemination of Polish science and contributes to education and national culture.

  • The conditional uncertainty principle based on majorization relations

    Author: - | Post date: 13.04.2016 | Last modified: 18.06.2018

    Date: 

    13/04/2016 - 13:15

    Speaker: 

    Waldemar Kłobus, Uniwersytet Adama Mickiewicza w Poznaniu

    We develop a general operational framework that formalizes the concept of conditional uncertainty in a measure-independent fashion. The formalism is built around a mathematical relation that we call conditional majorization. We define and characterize conditional majorization, and use it to develop tools for the construction of measures of the conditional uncertainty of individual measurements, and also of the joint conditional uncertainty of sets of measurements.

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  • Author: - | Post date: 04.02.2016 | Last modified: 18.06.2018

    Date: 

    09/03/2016 - 13:15

    Speaker: 

    Jarosław Duda, Uniwersytet Jagielloński
    Entropy coding is the heart of most of data compressors. Standard methods are Huffman coding - fast but inaccurate (suboptimal), and arithmetic/range coding - accurate but an order of magnitude slower (costly). I will tell about new approach: Asymmetric Numeral Systems, which is accurate while having cost similar to Huffman coding. It is for example used in Apple LZFSE (default compressor in iOS9 and OS X 10.11) or CRAM 3.0 DNA compressor of European Bioinformatics Institute.
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  • Nearest Neighbor Regression in the Presence of Bad Hubs

    Author: - | Post date: 02.02.2016 | Last modified: 18.06.2018

    Date: 

    04/02/2016 - 12:00

    Speaker: 

    Krisztian Buza, Budapest University of Technology and Economics

    Prediction on a numeric scale, i.e., regression, is one of the most prominent machine learning tasks with various applications in finance, medicine, social and natural sciences. Due to its simplicity, theoretical performance guarantees and successful real-world applications, one of the most popular regression techniques is the k nearest neighbor regression.

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