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|rss_image|  **2020-04 - 1/1** :ref:`Blog <ap-main-0>` :ref:`article (8) <ap-cat-article-0>` :ref:`articles (3) <ap-cat-articles-0>` :ref:`cours (6) <ap-cat-cours-0>` :ref:`module (7) <ap-cat-module-0>` :ref:`paper (3) <ap-cat-paper-0>`

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.. index:: 2020-04


.. _ap-month-2020-04-0:

2020-04 - 1/1
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.. blogpostagg::
    :title: Worst case scenarios
    :date: 2020-04-08
    :keywords: tree,branch bound
    :categories: papers
    :rawfile: 2020/2020-04-08_paper.rst

    J'ai découvert les publications de
    `Maria-Florina Balcan <https://www.csd.cs.cmu.edu/people/faculty/maria-balcan>`_
    aujourd'hui. Un de ses axes de recherches est - je cite -
    **Analysis of Algorithms beyond the Worst Case**.
    Celui-ci propose une amélioration de la construction d'arbres
    utilisés pour optimiser la recherche d'éléments dans
    un espace vectoriel : `Learning to Branch
    <http://www.cs.cmu.edu/~ninamf/papers/learn-branch.pdf>`_.
    Je recommande aussi les livres cités sur la page de son cours :
    `Advanced Introduction to Machine Learning <http://www.cs.cmu.edu/~10715-f18/>`_.
    Parmi ceux que j'ai envie de lire :
    `The Power of Localization
    for Efficiently Learning Linear Separators with Noise
    <http://www.cs.cmu.edu/~ninamf/papers/ABL16_malicious_jacm.pdf>`_,
    `Data Driven Resource Allocation for Distributed Learning
    <http://www.cs.cmu.edu/~ninamf/papers/data-driven-resource-allocation.pdf>`_,
    `Learning Valuation Functions
    <http://www.cs.cmu.edu/~ninamf/papers/learn-vals.pdf>`_,
    `Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
    <https://arxiv.org/pdf/1711.03091.pdf>`_.
    

    ...





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|rss_image|  **2020-04 - 1/1** :ref:`2022-10 (1) <ap-month-2022-10-0>` :ref:`2022-12 (2) <ap-month-2022-12-0>` :ref:`2023-01 (1) <ap-month-2023-01-0>` :ref:`2023-02 (1) <ap-month-2023-02-0>` :ref:`2023-04 (1) <ap-month-2023-04-0>`