1.7k citations · 2.1k across the 113 of their papers we have counts for
15 papers · 1 filter
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…
Multi-Armed Bandits with Censored Consumption of Resources
Viktor Bengs, Eyke Hüllermeier
We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corr…
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction
Stefan Heid, Marcel Wever, Eyke Hüllermeier
Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP…
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…
Learning Choice Functions via Pareto-Embeddings
Karlson Pfannschmidt, Eyke Hüllermeier
We consider the problem of learning to choose from a given set of objects, where each object is represented by a feature vector. Traditional approaches in choice modelling are main…
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
Alexander Tornede, Marcel Wever, Stefan Werner +2
Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem…
A Novel Higher-order Weisfeiler-Lehman Graph Convolution
Clemens Damke, Vitalik Melnikov, Eyke Hüllermeier
Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphis…
Learning Gradient Boosted Multi-label Classification Rules
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz +2
In multi-label classification, where the evaluation of predictions is less straightforward than in single-label classification, various meaningful, though different, loss functions…
On Aggregation in Ensembles of Multilabel Classifiers
Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp +2
While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members o…
Learning Tversky Similarity
Javad Rahnama, Eyke Hüllermeier
In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a…
Towards Analogy-Based Explanations in Machine Learning
Eyke Hüllermeier
Principles of analogical reasoning have recently been applied in the context of machine learning, for example to develop new methods for classification and preference learning. In…
Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control
Henrik Bode, Stefan Heid, Daniel Weber +2
Micro- and smart grids (MSG) play an important role both for integrating renewable energy sources in conventional electricity grids and for providing power supply in remote areas.…
Online Preselection with Context Information under the Plackett-Luce Model
Adil El Mesaoudi-Paul, Viktor Bengs, Eyke Hüllermeier
We consider an extension of the contextual multi-armed bandit problem, in which, instead of selecting a single alternative (arm), a learner is supposed to make a preselection in th…
Aleatoric and Epistemic Uncertainty with Random Forests
Mohammad Hossein Shaker, Eyke Hüllermeier
Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received i…
Extreme Algorithm Selection With Dyadic Feature Representation
Alexander Tornede, Marcel Wever, Eyke Hüllermeier
Algorithm selection (AS) deals with selecting an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem, e.g., choosing…