# Feature-aware Modulation for Learning from Temporal Tabular Data

Source: [Feature-aware Modulation for Learning from Temporal Tabular Data](<https://www.sota2.com/research/paper/6931157cd6b0dd3342933ae3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-05T05:03:13.504Z

Authors: Hao-Run Cai, Han-Jia Ye

Published: 2025-12-03T11:13:12.000Z

## Abstract

While tabular machine learning has achieved remarkable success, temporal distribution shifts pose significant challenges in real-world deployment, as the relationships between features and labels continuously evolve. Static models assume fixed mappings to ensure generalization, whereas adaptive models may overfit to transient patterns, creating a dilemma between robustness and adaptability. In this paper, we analyze key factors essential for constructing an effective dynamic mapping for temporal tabular data. We discover that evolving feature semantics-particularly objective and subjective meanings-introduce concept drift over time. Crucially, we identify that feature transformation strategies are able to mitigate discrepancies in feature representations across temporal stages. Motivated by these insights, we propose a feature-aware temporal modulation mechanism that conditions feature representations on temporal context, modulating statistical properties such as scale and skewness. By aligning feature semantics across time, our approach achieves a lightweight yet powerful adaptation, effectively balancing generalizability and adaptability. Benchmark evaluations validate the effectiveness of our method in handling temporal shifts in tabular data.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Tabular Prediction on TabReD (original temporal splits)](<https://www.sota2.com/research/sota/tabular-prediction-on-tabred-original-temporal-splits.md>): Tabular Prediction; TabReD (original temporal splits); HI AUC: 96.34.

- [Tabular Learning on TabReD](<https://www.sota2.com/research/sota/tabular-learning-on-tabred.md>): Tabular Learning; TabReD; Rank: 3.5.

- [Tabular Data Prediction on TabReD (random split)](<https://www.sota2.com/research/sota/tabular-data-prediction-on-tabred-random-split.md>): Tabular Data Prediction; TabReD (random split); HI: 0.9636.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.03678>)

[PDF](<https://arxiv.org/pdf/2512.03678.pdf>)