Efficient time stepping for numerical integration using reinforcement learning

1 code implementation • 8 Apr 2021 • Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, Karlson Pfannschmidt

While the classical schemes apply very generally and are highly efficient on regular systems, they can behave sub-optimal when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems.

Meta-Learning Numerical Integration +2

Learning Choice Functions via Pareto-Embeddings

no code implementations • 14 Jul 2020 • 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.

Learning Context-Dependent Choice Functions

1 code implementation • 29 Jan 2019 • Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst, Eyke Hüllermeier

Choice functions accept a set of alternatives as input and produce a preferred subset of these alternatives as output.

Deep Architectures for Learning Context-dependent Ranking Functions

1 code implementation • 15 Mar 2018 • Karlson Pfannschmidt, Pritha Gupta, Eyke Hüllermeier

Object ranking is an important problem in the realm of preference learning.

Object