8 citations · 17 across the 3 of their papers we have counts for
4 papers
Efficient time stepping for numerical integration using reinforcement learning
Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke +4
Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamic…
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…
Learning Context-Dependent Choice Functions
Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst +1
Choice functions accept a set of alternatives as input and produce a preferred subset of these alternatives as output. We study the problem of learning such functions under conditi…
Deep Architectures for Learning Context-dependent Ranking Functions
Karlson Pfannschmidt, Pritha Gupta, Eyke Hüllermeier
Object ranking is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects, which are typically represent…