FeatureLTE: Learning to Estimate Feature Importance
Summary: FeatureLTE: first pre-trained, general-purpose FIS estimator for tabular data. Learns meta-models from ~1k datasets to predict feature importance with quality comparable to SOTA, but up to 339x faster and robust/scalable on large inputs. (summarized by gpt-5.4-mini on May 24 2026)
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Authors
- 1. Tianping Zhang (Tsinghua University)
- 2. Zhaoyang Wang (Ant Financial)
- 3. Chen Qian (Ant Financial)
- 4. Jian Li (Tsinghua University)
- 5. Yin Lou (Ant Financial)
BibTeX Citation
@inproceedings{zhang_sigmod24,
title = {{FeatureLTE: Learning to Estimate Feature Importance}},
author = {Zhang, Tianping and Wang, Zhaoyang and Qian, Chen and Li, Jian and Lou, Yin},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654942},
url = {https://dl.acm.org/doi/10.1145/3654942},
year = {2024}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
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