NeurIPS 2020poster18 citations

Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent

Dimitris Fotakis, Thanasis Lianeas, Georgios Piliouras, Stratis Skoulakis

Abstract

We consider a natural model of online preference aggregation, where sets of preferred items R

BibTeX
@inproceedings{NEURIPS2020_5938b4d0,
 author = {Fotakis, Dimitris and Lianeas, Thanasis and Piliouras, Georgios and Skoulakis, Stratis},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {7816--7827},
 publisher = {Curran Associates, Inc.},
 title = {Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/5938b4d054136e5d59ada6ec9c295d7a-Paper.pdf},
 volume = {33},
 year = {2020}
}
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent · NeurIPS 2020