2 code implementations • 29 Jan 2024 • Erik Schultheis, Wojciech Kotłowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczyński
We consider the optimization of complex performance metrics in multi-label classification under the population utility framework.
2 code implementations • NeurIPS 2023 • Erik Schultheis, Marek Wydmuch, Wojciech Kotłowski, Rohit Babbar, Krzysztof Dembczyński
As such, it is characterized by long-tail labels, i. e., most labels have very few positive instances.
no code implementations • 26 Jul 2022 • Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczyński
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC).
no code implementations • 13 Mar 2022 • Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński, Willem Waegeman
Set-valued prediction is a well-known concept in multi-class classification.
1 code implementation • 20 Oct 2021 • Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar, Krzysztof Dembczyński
Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels.
1 code implementation • 8 Jul 2020 • Kalina Jasinska-Kobus, Marek Wydmuch, Devanathan Thiruvenkatachari, Krzysztof Dembczyński
We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels.
4 code implementations • 19 Jun 2019 • Thomas Mortier, Marek Wydmuch, Krzysztof Dembczyński, Eyke Hüllermeier, Willem Waegeman
In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee.
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 • ICML 2017 • Krzysztof Dembczyński, Wojciech Kotłowski, Oluwasanmi Koyejo, Nagarajan Natarajan
Statistical learning theory is at an inflection point enabled by recent advances in understanding and optimizing a wide range of metrics.
no code implementations • 27 Apr 2015 • Wojciech Kotłowski, Krzysztof Dembczyński
We show that the regret of the resulting classifier (obtained from thresholding $f$ on $\widehat{\theta}$) measured with respect to the target metric is upperbounded by the regret of $f$ measured with respect to the surrogate loss.