Research map / Adversarial robustness and security / Certified and empirical robustness
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach (ICML 2019 · similarity 0.91)
- Probably Approximately Global Robustness Certification (ICML 2025 · similarity 0.89)
- Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound (ICML 2025 · similarity 0.89)
- VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees (ICML 2024 · similarity 0.88)
- SURE: SUrvey REcipes for building reliable and robust deep networks (CVPR 2024 · similarity 0.87)
- Certified but Private: Scalable Zero-Knowledge Proofs for the Formal Verification of Neural Networks (NeurIPS 2026 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.