Interpretable Attribute Discretization
Summary: Defines attribute discretization as jointly optimizing task utility and human semantic fidelity, exposing their Pareto frontier. An RL policy searches millions of partitions via only 200 candidates, reducing frontier error 2–4× across four tasks. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Eugenie Lai (Massachusetts Institute of Technology)
- 2. Inbal Croitoru (Technion)
- 3. Brit Youngmann (Technion)
- 4. Sainyam Galhotra (Cornell University)
- 5. El Kindi Rezig (University of Utah)
- 6. Michael Cafarella (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{lai_sigmod26,
title = {{Interpretable Attribute Discretization}},
author = {Lai, Eugenie and Croitoru, Inbal and Youngmann, Brit and Galhotra, Sainyam and Rezig, El Kindi and Cafarella, Michael},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802075},
url = {https://dl.acm.org/doi/10.1145/3802075},
year = {2026}
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| 410 | SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics | 2015 | VLDB | 0.0001890421 |
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