VIP-COP: Context Optimization for Tabular Foundation Models
Tabular foundation models (TFMs) have emerged as a powerful paradigm for in-context learning on structured data, enabling direct prediction on new tabular tasks without task-specific training. However, their effectiveness is constrained by context length limits, restricting application to medium-scale data and degrading performance when inference-time data exceed pretraining size distributions. Our work introduces VIP-COP, estimating the Value of Importance for Prediction of training examples and features for hard Context OPtimization for TFMs. Its explicit selection mechanism suppresses noise and isolates influential data, enabling the model to also benefit from data augmentation by prio...
| Authors | Yilong Chen, Xueying Ding, Leman Akoglu |
|---|---|
| Primary category | cs.LG |
| Published | 13 May 2026 |
| Last updated at source | 29 September 2026 |
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