Christian Geissler's research while affiliated with Technische Universität Berlin and other places

Publications (2)

Full-text available
Algorithm selection (AS) tasks are dedicated to find the optimal algorithm for an unseen problem instance. With the knowledge of problem instances’ meta-features and algorithms’ landmark performances, Machine Learning (ML) approaches are applied to solve AS problems. However, the standard training process of benchmark ML approaches in AS either nee...
Full-text available
Motivated by the problem of tuning hyperparameters in machine learning, we present a new approach for gradually and adaptively optimizing an unknown function using estimated gradients. We validate the empirical performance of the proposed idea on both low and high dimensional problems. The experimental results demonstrate the advantages of our appr...


... In this special issue, four papers address these aspects, going beyond accuracy as the sole metric. Yuan et al. [80] introduce the concept of learning to rank into recommender systems. They embed bi-linear factorization to model algorithm performances, achieving a trade-off between accuracy and inference time in algorithm selection. ...