20 citations · 40 across the 4 of their papers we have counts for
6 papers · 1 filter
Propensity-scored Probabilistic Label Trees
Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar +1
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. R…
Probabilistic Label Trees for Extreme Multi-label Classification
Kalina Jasinska-Kobus, Marek Wydmuch, Krzysztof Dembczynski +2
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. Pro…
Efficient Set-Valued Prediction in Multi-Class Classification
Thomas Mortier, Marek Wydmuch, Krzysztof Dembczyński +2
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. More precisely, th…
On the computational complexity of the probabilistic label tree algorithms
Robert Busa-Fekete, Krzysztof Dembczynski, Alexander Golovnev +4
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…
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov +2
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 l…
Consistent Multilabel Ranking through Univariate Losses
Krzysztof Dembczynski, Wojciech Kotlowski, Eyke Huellermeier
We consider the problem of rank loss minimization in the setting of multilabel classification, which is usually tackled by means of convex surrogate losses defined on pairs of labe…