Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Fekete, Robert"'
Autor:
Busa-Fekete, Róbert István, Dick, Travis, Gentile, Claudio, Medina, Andrés Muñoz, Smith, Adam, Swanberg, Marika
We propose reconstruction advantage measures to audit label privatization mechanisms. A reconstruction advantage measure quantifies the increase in an attacker's ability to infer the true label of an unlabeled example when provided with a private ver
Externí odkaz:
http://arxiv.org/abs/2406.02797
Autor:
Bacis, Enrico, Bilogrevic, Igor, Busa-Fekete, Robert, Herath, Asanka, Sartori, Antonio, Syed, Umar
Modern Web APIs allow developers to provide extensively customized experiences for website visitors, but the richness of the device information they provide also make them vulnerable to being abused to construct browser fingerprints, device-specific
Externí odkaz:
http://arxiv.org/abs/2403.15607
Autor:
Bravo-Hermsdorff, Gecia, Busa-Fekete, Róbert, Ghavamzadeh, Mohammad, Medina, Andres Muñoz, Syed, Umar
Modern statistical estimation is often performed in a distributed setting where each sample belongs to a single user who shares their data with a central server. Users are typically concerned with preserving the privacy of their samples, and also wit
Externí odkaz:
http://arxiv.org/abs/2305.07751
Autor:
Busa-Fekete, Robert Istvan, Choi, Heejin, Dick, Travis, Gentile, Claudio, medina, Andres Munoz
We consider the problem of Learning from Label Proportions (LLP), a weakly supervised classification setup where instances are grouped into "bags", and only the frequency of class labels at each bag is available. Albeit, the objective of the learner
Externí odkaz:
http://arxiv.org/abs/2302.03115
Autor:
Bravo-Hermsdorff, Gecia, Busa-Fekete, Robert, Gunderson, Lee M., Medina, Andrés Munõz, Syed, Umar
Data anonymization is an approach to privacy-preserving data release aimed at preventing participants reidentification, and it is an important alternative to differential privacy in applications that cannot tolerate noisy data. Existing algorithms fo
Externí odkaz:
http://arxiv.org/abs/2201.12306
Autor:
Jasinska-Kobus, Kalina, Wydmuch, Marek, Dembczynski, Krzysztof, Kuznetsov, Mikhail, Busa-Fekete, Robert
Extreme multi-label classification (XMLC) is a learning task of tagging instances with a small subset of relevant labels chosen from an extremely large pool of possible labels. Problems of this scale can be efficiently handled by organizing labels as
Externí odkaz:
http://arxiv.org/abs/2009.11218
The Mallows model, introduced in the seminal paper of Mallows 1957, is one of the most fundamental ranking distribution over the symmetric group $S_m$. To analyze more complex ranking data, several studies considered the Generalized Mallows model def
Externí odkaz:
http://arxiv.org/abs/1906.01009
Autor:
Busa-Fekete, Robert, Dembczynski, Krzysztof, Golovnev, Alexander, Jasinska, Kalina, Kuznetsov, Mikhail, Sviridenko, Maxim, Xu, Chao
Label tree-based algorithms are widely used to tackle multi-class and multi-label problems with a large number of labels. We focus on a particular subclass of these algorithms that use probabilistic classifiers in the tree nodes. Examples of such alg
Externí odkaz:
http://arxiv.org/abs/1906.00294
Web crawling is the problem of keeping a cache of webpages fresh, i.e., having the most recent copy available when a page is requested. This problem is usually coupled with the natural restriction that the bandwidth available to the web crawler is li
Externí odkaz:
http://arxiv.org/abs/1905.12781
Autor:
Wydmuch, Marek, Jasinska, Kalina, Kuznetsov, Mikhail, Busa-Fekete, Róbert, Dembczyński, Krzysztof
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. Large label spaces can be efficiently handled by organizing labels as a tree,
Externí odkaz:
http://arxiv.org/abs/1810.11671