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20222026
most citedOn Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

20 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?

Gaetan De Waele, Marek Wydmuch, Krzysztof Dembczyński +2

One of the central challenges in the computational analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data is to identify the compounds underlying the output spe…

cs.LG2024

A General Online Algorithm for Optimizing Complex Performance Metrics

Wojciech Kotłowski, Marek Wydmuch, Erik Schultheis +2

We consider sequential maximization of performance metrics that are general functions of a confusion matrix of a classifier (such as precision, F-measure, or G-mean). Such metrics…

cs.LG2024

Consistent algorithms for multi-label classification with macro-at- metrics

Erik Schultheis, Wojciech Kotłowski, Marek Wydmuch +3

We consider the optimization of complex performance metrics in multi-label classification under the population utility framework. We mainly focus on metrics linearly decomposable i…

cs.LG2023

Generalized test utilities for long-tail performance in extreme multi-label classification

Erik Schultheis, Marek Wydmuch, Wojciech Kotłowski +2

Extreme multi-label classification (XMLC) is the task of selecting a small subset of relevant labels from a very large set of possible labels. As such, it is characterized by long-…

cs.LG2022★ 20 cited

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

Erik Schultheis, Marek Wydmuch, Rohit Babbar +1

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). In th…