arXiv Machine Learning By Youwei Zhong, Ben Merbaum, Timos Antonopoulos, Ning Luo, Charalampos Papamanthou, Katerina Sotiraki, Ruzica Piskac

Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees

Read the original on arXiv Machine Learning →

arXiv:2608. 17070v1 Announce Type: new Abstract: With the growing deployment of machine learning models, formal guarantees of the robustness and fairness of these models have become increasingly important in safety-critical and legal-compliance settings.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
Jul 7

Privacy-Preserving Robustness Verification for Neural Networks

arXiv:2607. 05251v1 Announce Type: cross Abstract: Neural network verification and data privacy are inherently in tension: verification demands full access to model parameters and input data, yet both are increasingly restricted by privacy regulations and intellectual property constraints.

By Nianyun Song, Xiaokun Luan, Yu Guo, Rongfang Bie, Meng Sun, Xiyue Zhang
arXiv AI
Jun 29

Halt Fast! Early Stopping for Certified Robustness

arXiv:2606. 27694v1 Announce Type: cross Abstract: Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited by extreme computational costs.

By Andrew C. Cullen, Paul Montague, Benjamin I. P. Rubinstein