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cs.LG2026
Thinking in Depth: Retrospective Inference for Tabular Foundation Models
Hao-Run Cai, Si-Yang Liu, Zi-Jian Cheng +8
Tabular foundation models (TFMs) are pretrained across diverse tabular tasks and make predictions on a new table at inference time using its labeled examples as context. Most recen…
cs.LG2026
On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
Zhi Zhou, Ming Yang, Shi-Yu Tian +3
Test-time adaptation (TTA) aims to adapt models to maintain reliable performance on non-stationary test streams without requiring labeled data. Despite its empirical success, the l…
cs.LG2024
Fully Test-time Adaptation for Tabular Data
Zhi Zhou, Kun-Yang Yu, Lan-Zhe Guo +1
Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still stru…