Research map / Federated learning and privacy / Differential privacy
INSPECTRE: Privately Estimating the Unseen
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Laplace noise to the Smoothed Good-Toulmin estimator | Support coverage estimation · Synthetic data, k=20000 | RMSE | No clear effect |
| Add Laplace noise to the Smoothed Good-Toulmin estimator | Support coverage estimation · 2000 US Census last names | RMSE | No clear effect |
| Add Laplace noise to the Smoothed Good-Toulmin estimator | Support coverage estimation · Hamlet words | RMSE | No clear effect |
| Add Laplace noise to the polynomial entropy estimator | Polynomial approximation entropy estimator · Synthetic distributions, k=1000, ε=1 | RMSE | No clear effect |
| Use the polynomial approximation estimator instead of plug-in or Miller-Madow | Entropy estimation · Synthetic distributions, k=1000, ε=1 | RMSE | Helped |
| Add Laplace noise to the Smoothed Good-Toulmin estimator | Support coverage estimation · Smaller-support synthetic instances | RMSE | Hurt |
Closest papers by meaning
- Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases (NeurIPS 2019 · similarity 0.91)
- Privacy Guarantees in Posterior Sampling under Contamination (NeurIPS 2026 · similarity 0.89)
- Auditing $f$-differential privacy in one run (ICML 2025 · similarity 0.88)
- Instance-Specific Asymmetric Sensitivity in Differential Privacy (NeurIPS 2024 · similarity 0.88)
- Exactly Minimax-Optimal Locally Differentially Private Sampling (NeurIPS 2024 · similarity 0.88)
- Optimal Survey Design for Private Mean Estimation (ICML 2025 · similarity 0.88)
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