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S. Goldwasser

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#artificial intelligence Preprint Apr 2025

Does More Compute Make Every Adversarial Attack Transferable?

This work shows that, when there is a quadratic gap in computational resources between attacker and defender, every learnable classification task admits either a transferable attack or an adversarial defense, and proves a stronger result that implies this failure.

Greg Gluch, S. Goldwasser, Anastasiia Struss · 1 citation
#artificial intelligence Preprint Aug 2026

How to Verify Probabilistic Consistency of Predictive Models

The interactive PCP is constructed, which shows a protocol in which a polynomial-time verifier can verify the approximate consistency of (P,Q), and places l_2-approximate probabilistic consistency of explicit claims in NP, with certificates of length O(mn + log B) in the input bit-precision B.

Orr Paradise, Oliver E. Richardson, Y. Bengio et al. · 0 citations

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