| Paper ID | D3-S1-T3.1 |
| Paper Title |
Neural Network-based Estimation of the MMSE |
| Authors |
Mario Diaz, Universidad Nacional Autónoma de México, Mexico; Peter Kairouz, Google AI, United States; Jiachun Liao, Lalitha Sankar, Arizona State University, United States |
| Session |
D3-S1-T3: Neural Estimation |
| Chaired Session: |
Wednesday, 14 July, 22:00 - 22:20 |
| Engagement Session: |
Wednesday, 14 July, 22:20 - 22:40 |
| Abstract |
The minimum mean-square error (MMSE) achievable by optimal estimation of a random variable $S$ given another random variable $T$ is of much interest in a variety of statistical contexts. Motivated by a growing interest in auditing machine learning models for unintended information leakage, we propose a neural network-based estimator of this MMSE. We derive a lower bound for the MMSE based on the proposed estimator and the Barron constant associated with the conditional expectation of $S$ given $T$. Since the latter is typically unknown in practice, we derive a general bound for the Barron constant that produces order optimal estimates for canonical distribution models.
|
