Scaling of Unlabeled Tabular Data and Certified Robustness in Self-Supervised Representations

Abstract

Abstract: Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model's decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have

Research Question

How does the scaling of unlabeled tabular data impact the certified robustness radius of self-supervised representations trained via context prediction tasks?

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Automated triage signals derived from public fields; not human peer review or independent validation.

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance