A new framework evaluates privacy metrics for synthetic tabular data by inserting controlled risks and testing detection under no-box threat models on public datasets.
Failure modes in machine learning systems
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REVELIO uncovers interpretable failure modes in VLMs by searching combinatorial concept spaces with diversity-aware beam search and Gaussian-process Thompson sampling, revealing vulnerabilities in autonomous driving and indoor robotics.
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Revealing Interpretable Failure Modes of VLMs
REVELIO uncovers interpretable failure modes in VLMs by searching combinatorial concept spaces with diversity-aware beam search and Gaussian-process Thompson sampling, revealing vulnerabilities in autonomous driving and indoor robotics.