no code implementations • 12 May 2023 • Gecia Bravo-Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh, Andres Muñoz Medina, Umar Syed
For a joint distribution over many variables whose conditional independence is given by a tree, we describe algorithms for estimating Shannon entropy that require a number of samples that is linear in the number of variables, compared to the quadratic sample complexity of prior work.
no code implementations • NeurIPS 2021 • Róbert Busa-Fekete, Dimitris Fotakis, Balazs Szorenyi, Emmanouil Zampetakis
In this paper, we devise identity tests for ranking data that is generated from Mallows model both in the \emph{asymptotic} and \emph{non-asymptotic} settings.
no code implementations • NeurIPS 2021 • Róbert Busa-Fekete, Dimitris Fotakis, Emmanouil Zampetakis
We study the problem of uniformity testing for statistical data that consists of rankings over $m$ items where the alternative class is restricted to Mallows models with single parameter.
no code implementations • 3 Jun 2019 • Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis
The main result of the paper is a tight sample complexity bound for learning Mallows and Generalized Mallows Model.
1 code implementation • NeurIPS 2018 • Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczyński
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.
no code implementations • NeurIPS 2015 • Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier
In this paper, we study the problem of F-measure maximization in the setting of online learning.
no code implementations • NeurIPS 2015 • Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier
We study the problem of online rank elicitation, assuming that rankings of a set of alternatives obey the Plackett-Luce distribution.