Learning for data with heterogeneity (ICDM'21 Best Paper Award)
[ICDM'21] Yiqun Xie*, Erhu He*, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh and Praveen Ravirathinam. A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity. IEEE International Conference on Data Mining (ICDM'21), 2021.
[IJCAI'22] Yiqun Xie*, Erhu He*, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh and Praveen Ravirathinam. Statistically-Guided Deep Network Transformation to Harness Heterogeneity in Space (Extended Abstract). The 31st International Joint Conference on Artificial Intelligence (IJCAI'22), Sister Conference Best Paper Track. Invited. 2022.
@inproceedings{xie2021statistically,
title={A statistically-guided deep network transformation and moderation framework for data with spatial heterogeneity},
author={Xie, Yiqun and He, Erhu and Jia, Xiaowei and Bao, Han and Zhou, Xun and Ghosh, Rahul and Ravirathinam, Praveen},
booktitle={2021 IEEE International Conference on Data Mining (ICDM)},
pages={767--776},
year={2021},
organization={IEEE}
}
@inproceedings{xie2022statistically,
title = {Statistically-Guided Deep Network Transformation to Harness Heterogeneity in Space (Extended Abstract)},
author = {Xie, Yiqun and He, Erhu and Jia, Xiaowei and Bao, Han and Zhou, Xun and Ghosh, Rahul and Ravirathinam, Praveen},
booktitle = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}},
pages = {5364--5368},
year = {2022},
note = {Sister Conferences Best Papers}
}
Example dataset 1 (crop monitoring):
- X_example: Features
- y_example: Labels
- Short description (more coming)
- X is a satellite imagery with 10 spectral bands, and y contains 23 raw crop classes (e.g., soybean)
- In this example spatial locations are represented by row and column IDs
Running heterogeneity-aware learning on (X_example, y_example)
More documentation and examples for usage will be added soon.