20 citations · 88 across the 16 of their papers we have counts for
35 papers
Set-valued prediction in hierarchical classification with constrained representation complexity
Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński +1
Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of class…
Non-Stationary Dueling Bandits
Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier
We study the non-stationary dueling bandits problem with arms, where the time horizon consists of stationary segments, each of which is associated with its own preferen…
Machine Learning for Online Algorithm Selection under Censored Feedback
Alexander Tornede, Viktor Bengs, Eyke Hüllermeier
In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best al…
Gradient-based Label Binning in Multi-label Classification
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz +1
In multi-label classification, where a single example may be associated with several class labels at the same time, the ability to model dependencies between labels is considered c…
Learning Structured Declarative Rule Sets -- A Challenge for Deep Discrete Learning
Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía +1
Arguably the key reason for the success of deep neural networks is their ability to autonomously form non-linear combinations of the input features, which can be used in subsequent…
Conformal Rule-Based Multi-label Classification
Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencia
We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rul…