NeurIPS 2025

Feature-aware Modulation for Learning from
Temporal Tabular Data
Hao-Run Cai
Han-Jia Ye
School of Artificial Intelligence, Nanjing University, China
National Key Laboratory for Novel Software Technology, Nanjing University, China
{caihr, yehj}@lamda.nju.edu.cn
Abstract
While tabular machine learning has achieved remarkable success, temporal distribu-
tion shifts pose significant challenges in real-world deployment, as the relationships
between features and labels continuously evolve. Static models assume fixed map-
pings 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.
1
Introduction
Tabular data, structured in rows and columns, forms the foundation of critical decision-making across
diverse domains such as healthcare [39], finance [42], and e-commerce [33]. Traditional machine
learning models for tabular data—including tree-based methods [5, 7, 22, 36] and modern deep
architectures [2, 10–13, 16–18, 52]—commonly assume independent and identically distributed
(i.i.d.) data [3, 30]. However, in real-world scenarios [54],...