Tabular foundation models suffer from test-time adversarial vulnerabilities that degrade accuracy and enable transferable attacks, but incremental adversarial in-context learning improves robustness on multiple benchmarks.
In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, Lud De Raedt (Ed.)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Tabular foundation models suffer from test-time adversarial vulnerabilities that degrade accuracy and enable transferable attacks, but incremental adversarial in-context learning improves robustness on multiple benchmarks.