Von Mises-Fisher Sampling of GloVe Vectors
Abstract
A recent publication introduced von Mises-Fisher exploration (vMF-exp), a scalable sampling method for exploring large action sets in reinforcement learning problems where hyperspherical embedding vectors represent these actions. We present the first experimental validation of vMF-exp’s key theoretical and scalability properties on a publicly available real-world dataset, confirming the potential of this method.
Cite
Text
Bendada et al. "Von Mises-Fisher Sampling of GloVe Vectors." ICLR 2025 Workshops: FPI, 2025.Markdown
[Bendada et al. "Von Mises-Fisher Sampling of GloVe Vectors." ICLR 2025 Workshops: FPI, 2025.](https://mlanthology.org/iclrw/2025/bendada2025iclrw-von/)BibTeX
@inproceedings{bendada2025iclrw-von,
title = {{Von Mises-Fisher Sampling of GloVe Vectors}},
author = {Bendada, Walid and Salha-Galvan, Guillaume and Hennequin, Romain and Bontempelli, Théo and Bouabça, Thomas and Cazenave, Tristan},
booktitle = {ICLR 2025 Workshops: FPI},
year = {2025},
url = {https://mlanthology.org/iclrw/2025/bendada2025iclrw-von/}
}