arXiv AI By Orr Paradise, Oliver Richardson, Yoshua Bengio, Shafi Goldwasser

How to Verify Probabilistic Consistency of Predictive Models

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The paper presents an interactive probabilistically checkable proof (PCP) protocol that allows a polynomial‑time verifier to check the approximate consistency of a probabilistic predictor defined by two circuits, P and Q. By evaluating these circuits at a few points and querying a proof oracle that encodes a witnessing probability distribution, the verifier can confirm that the predictor’s many conditional‑probability claims are self‑consistent. The authors also establish that the problem of verifying l₂‑approximate consistency for explicit probabilistic claims lies in NP, with certificates of size O(mn + log B), and show how to eliminate dependence on the input bit‑precision B through a small additive gap.

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arXiv AI
Aug 12

How to Verify Consistency of Probabilistic Claims

arXiv:2608. 11181v1 Announce Type: cross Abstract: When a probabilistic predictor answers many conditional-probability queries, are its answers self-consistent, and can this be verified in polynomial time?

By Orr Paradise, Oliver Richardson, Yoshua Bengio, Shafi Goldwasser