DIWIFT: Discovering Instance-wise Influential Features for Tabular Data.
Dugang Liu|Pengxiang Cheng|Hong Zhu|Xing Tang|Yanyu Chen|Xiaoting Wang|Weike Pan|Zhong Ming|Xiuqiang He
| Anthology ID: | DBLP:conf/www/Liu0Z0CWP0023 |
|---|---|
| Volume: | Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 |
| Year: | 2023 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM |
| Pages: | 1673-1682 |
| URL: | https://doi.org/10.1145/3543507.3583382 |
| DOI: | https://doi.org/10.1145/3543507.3583382 |
| DBLP: | conf/www/Liu0Z0CWP0023 |
| BibTeX: |
@inproceedings{liu-2023-diwift,
author = {Dugang Liu and
Pengxiang Cheng and
Hong Zhu and
Xing Tang and
Yanyu Chen and
Xiaoting Wang and
Weike Pan and
Zhong Ming and
Xiuqiang He},
editor = {Ying Ding and
Jie Tang and
Juan F. Sequeda and
Lora Aroyo and
Carlos Castillo and
Geert-Jan Houben},
title = {{DIWIFT: Discovering Instance-wise Influential Features for Tabular Data}},
booktitle = {{Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}},
pages = {1673--1682},
publisher = {ACM},
year = {2023},
url = {https://doi.org/10.1145/3543507.3583382},
doi = {https://doi.org/10.1145/3543507.3583382}
}