Summary of Adaptable: Test-time Adaptation For Tabular Data Via Shift-aware Uncertainty Calibrator and Label Distribution Handler, by Changhun Kim et al.
AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler
by Changhun Kim, Taewon Kim, Seungyeon Woo, June Yong Yang, Eunho Yang
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AdapTable framework is a novel test-time adaptation (TTA) method for tabular data that addresses the challenges of handling distribution shifts in this domain. The framework operates in two stages: first, it calibrates model predictions using a shift-aware uncertainty calibrator, and then adjusts these predictions to match the target label distribution with a label distribution handler. This approach is particularly useful for privacy-sensitive tabular domains where access to source data is not feasible. Experimental results demonstrate AdapTable’s ability to handle various real-world distribution shifts, achieving up to a 16% improvement on the HELOC dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a new way to help machine learning models adapt to changes in data without having access to the original information. This is important because often, we don’t have permission to share or use the old data, but we still want our models to work well with the new data. The new approach, called AdapTable, works by first making sure the model’s predictions are accurate and then adjusting those predictions to match the new data. The team tested this method on several different scenarios and found that it can improve performance by up to 16%. |
Keywords
* Artificial intelligence * Machine learning
