Research map / Adversarial robustness and security / Certified and empirical robustness
On Collective Robustness of Bagging Against Data Poisoning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace vanilla random subsampling with deterministic hash-based subsampling | Bagging · Bank, 5% poison budget, G=20 | certified collective robustness: 4449 -> 9718 | Helped |
| Replace vanilla random subsampling with deterministic hash-based subsampling | Bagging · Electricity, 5% poison budget, G=20 | certified collective robustness: 9348 -> 9915 | Helped |
| Replace vanilla random subsampling with deterministic hash-based subsampling | Bagging · FMNIST and CIFAR-10, 5% poison budget, G=50 | certified collective robustness: FMNIST: 7727 -> 9726; CIFAR-10: 3621 -> 8393 | Helped |
| Use collective certification instead of sample-wise certification | Hash bagging · Bank, 5% poison budget, G=20 | certified collective robustness: 9599 -> 9718 | Helped |
| Use collective certification with decomposition instead of collective certification alone (ablation) | Hash bagging · FMNIST and CIFAR-10, 5% poison budget, G=50 | certified collective robustness: FMNIST: 9726 -> 9666; CIFAR-10: 8393 -> 8694 | Helped |
| Increase the number of sub-trainsets G toward GK=N (ablation) | Hash bagging · Certified collective robustness across tested G values | certified collective robustness | Mixed |
| Increase sub-trainset size K toward K=N/G (ablation) | Hash bagging · Certified collective robustness across tested K values | certified collective robustness | Mixed |
| Increase the sub-testset scale Δ (ablation) | Hash bagging certification · Certified collective robustness without a time limit | certified collective robustness | Helped |
| Increase the sub-testset scale Δ under a 2s-per-sample time limit (ablation) | Hash bagging certification · Certified collective robustness with limited solver time | certified collective robustness | Mixed |
Closest papers by meaning
- Collective Certified Robustness against Graph Injection Attacks (ICML 2024 · similarity 0.83)
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- Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy (NeurIPS 2026 · similarity 0.82)
- RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation (NeurIPS 2024 · similarity 0.82)
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