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LLMClean: Context-Aware Tabular Data Cleaning via LLM-Generated OFDs
Fabian Biester, Mohamed Abdelaal, Daniel Del Gaudio
https://arxiv.org/abs/2404.18681 h…

LLMClean: Context-Aware Tabular Data Cleaning via LLM-Generated OFDs
Machine learning's influence is expanding rapidly, now integral to decision-making processes from corporate strategy to the advancements in Industry 4.0. The efficacy of Artificial Intelligence broadly hinges on the caliber of data used during its training phase; optimal performance is tied to exceptional data quality. Data cleaning tools, particularly those that exploit functional dependencies within ontological frameworks or context models, are instrumental in augmenting data quality. Neverth…

This https://arxiv.org/abs/2312.07577 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Benchmarking Distribution Shift in Tabular Data with TableShift
Robustness to distribution shift has become a growing concern for text and image models as they transition from research subjects to deployment in the real world. However, high-quality benchmarks for distribution shift in tabular machine learning tasks are still lacking despite the widespread real-world use of tabular data and differences in the models used for tabular data in comparison to text and images. As a consequence, the robustness of tabular models to distribution shift is poorly under…

OmniMatch: Effective Self-Supervised Any-Join Discovery in Tabular Data Repositories
Christos Koutras, Jiani Zhang, Xiao Qin, Chuan Lei, Vasileios Ioannidis, Christos Faloutsos, George Karypis, Asterios Katsifodimos
https://arxiv.org/abs/2403.07653

OmniMatch: Effective Self-Supervised Any-Join Discovery in Tabular Data Repositories
How can we discover join relationships among columns of tabular data in a data repository? Can this be done effectively when metadata is missing? Traditional column matching works mainly rely on similarity measures based on exact value overlaps, hence missing important semantics or failing to handle noise in the data. At the same time, recent dataset discovery methods focusing on deep table representation learning techniques, do not take into consideration the rich set of column similarity sign…