Search Results for author: Róbert Busa-Fekete

Found 7 papers, 1 papers with code

Private and Communication-Efficient Algorithms for Entropy Estimation

no code implementations12 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.

Identity testing for Mallows model

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.

Private and Non-private Uniformity Testing for Ranking Data

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.

Optimal Learning of Mallows Block Model

no code implementations3 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.

A no-regret generalization of hierarchical softmax to extreme multi-label classification

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.

Extreme Multi-Label Classification General Classification

Online F-Measure Optimization

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.

Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach

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.

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