Anan Kabaha
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- Dana Drachsler Cohen10 talks
- Aaron Roth11 talks
- Aws Albarghouthi14 talks
- Sharmistha Chatterjee6 talks
- Zoubin Ghahramani3 talks
- Rob Romijnders3 talks
| title | conference | date |
|---|---|---|
| Guarding the Privacy of Label-Only Access to Neural Network Classifiers via Formal(…) | OOPSLA 2025 | 2025-10-16 |
Claims a practical verifier, LUCID, that computes an iDP-DB overapproximation so noise is added only to inputs that actually risk leaking labels; it encodes… | ||
| Quantization with Guaranteed Floating-Point Neural Network Classifications | OOPSLA 2025 | 2025-10-16 |
CoMPAQt computes a maximal classification-confidence bound using a novel MILP encoding of quantization plus customized linear relaxations and constraints that… | ||
| Verification of Neural Networks' Global Robustness | OOPSLA 2024 | 2024-10-20 |
Introduces Delta Global robustness as the minimal confidence margin guaranteeing any input stays correctly classified under a given perturbation, and builds an… | ||
| Verification of Neural Networks’ Local Differential Classification Privacy | VMCAI 2024 | 2024-01-15 |
Defines local differential classification privacy for black-box classifiers and builds Sphynx, an algorithm that predicts an interval-based abstract “hyper… | ||
| Maximal Robust Neural Network Specifications via Oracle-guided Numerical Optimi... | VMCAI 2023 | 2023-01-15 |
MaRVeL computes maximal non-uniform robust specifications that explicitly maximize a chosen norm by coupling a numerical optimizer with a non-differentiable… | ||
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