NeurIPS 2018poster17 citations
Mallows Models for Top-k Lists
Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi
Abstract
The classic Mallows model is a widely-used tool to realize distributions on per- mutations. Motivated by common practical situations, in this paper, we generalize Mallows to model distributions on top-k lists by using a suitable distance measure between top-k lists. Unlike many earlier works, our model is both analytically tractable and computationally efficient. We demonstrate this by studying two basic problems in this model, namely, sampling and reconstruction, from both algorithmic and experimental points of view.
BibTeX
@inproceedings{NEURIPS2018_a381c2c3,
author = {Chierichetti, Flavio and Dasgupta, Anirban and Haddadan, Shahrzad and Kumar, Ravi and Lattanzi, Silvio},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Mallows Models for Top-k Lists},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/a381c2c35c9157f6b67fd07d5a200ae1-Paper.pdf},
volume = {31},
year = {2018}
}