Learning-Augmented Query Policies for Minimum Spanning Tree with Uncertainty
(2022)
Conference Proceeding
Erlebach, T., de Lima, M. S., Megow, N., & Schlöter, J. (2022). Learning-Augmented Query Policies for Minimum Spanning Tree with Uncertainty. In S. Chechik, G. Navarro, E. Rotenberg, & G. Herman (Eds.), 30th Annual European Symposium on Algorithms (ESA 2022) (12:1-12:16). https://doi.org/10.4230/lipics.esa.2022.12
We study how to utilize (possibly erroneous) predictions in a model for computing under uncertainty in which an algorithm can query unknown data. Our aim is to minimize the number of queries needed to solve the minimum spanning tree problem, a fundam... Read More about Learning-Augmented Query Policies for Minimum Spanning Tree with Uncertainty.