NeurIPS 2025 Submission

Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, their robustness to adversarial manipulation remains largely unexplored.

Publication Details

Journal
Figshare
Published
2025-01-01
DOI
https://doi.org/10.6084/m9.figshare.29086586.v1
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

NeurIPS 2025 Submission

Figshare
Adversarial Robustness in Machine Learning
article

NeurIPS 2025 Submission

article en

Abstract

Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, their robustness to adversarial manipulation remains largely unexplored.

Figshare
Openalex Percentile: Top 77%
Adversarial Robustness in Machine Learning
DOISourceOpenAlex
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