arXiv Machine Learning
Exploring Solver-Level Warmstarting for Neural Network Verification
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arXiv Machine Learning
Aug 19
Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees
PANDA is a scalable system that uses zero‑knowledge proofs to certify the robustness and fairness of neural networks without revealing their private parameters. Built on the CROWN robustness framework, PANDA introduces a novel algorithm for proving linear relaxation bounds on non‑linear activation layers, producing lightweight proofs. The system can generate proofs for networks with over 2.9 million parameters in just five minutes and verify them in ten seconds, scaling polynomially with network size and enabling verification of models four orders of magnitude larger than prior ZKP‑based approaches.
arXiv AI
Aug 3
Learning Lookahead Lemmas for Neural Network Verification
arXiv:2607. 29051v1 Announce Type: cross Abstract: State-of-the-art neural network verifiers use the branch-and-bound procedure as their core solving mechanism.
arXiv AI
Jul 21
Lookahead Branching for Neural Network Verification
arXiv:2607. 17290v1 Announce Type: cross Abstract: In this work, we investigate the effect of lookahead branching strategies in neural network verification.
arXiv Machine Learning
Jun 4
veriFIRE: an Industrial Case Study in Verifying Consistency Properties for a DNN-Based Wildfire Detection System
arXiv:2606. 04121v1 Announce Type: cross Abstract: We present our ongoing work on the veriFIRE project: a collaboration between industry and academia, aimed at applying verification to increase the reliability of a real-world, safety-critical system.
arXiv Machine Learning
Sep 22
PICID: Proof-Driven Clause Learning in Neural Network Verification
arXiv:2503.12083v3 Announce Type: replace-cross Abstract: Current Deep Neural Network (DNN) verifiers are typically designed to prioritize scalability over reliability. Reliability can be reinforced...
arXiv Machine Learning
Aug 11
Floating-Point Neural Network Verification at the Software Level
arXiv:2510. 23389v2 Announce Type: replace-cross Abstract: The behaviour of neural network components must be proven correct before deployment in safety-critical systems.