# Data Amplification: A Unified and Competitive Approach to Property Estimation

arXiv: https://arxiv.org/abs/1904.00070 · HTML: https://mlsanity.com/paper/1904.00070/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Generative Models for Detector Signature Simulation: A Taxonomic Review](https://mlsanity.com/paper/2312.09597/) (2023)
- [Auditing Fairness under Model Updates: Fundamental Complexity and Property-Preserving Updates](https://mlsanity.com/paper/2601.05909/) (2026)
- [Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)](https://mlsanity.com/paper/2401.05295/) (2024)
- [Partitioning the Sample Space for a More Precise Shannon Entropy Estimation](https://mlsanity.com/paper/2512.10133/) (2025)

## Abstract (arXiv)

Estimating properties of discrete distributions is a fundamental problem in statistical learning. We design the first unified, linear-time, competitive, property estimator that for a wide class of properties and for all underlying distributions uses just 2n samples to achieve the performance attained by the empirical estimator with n√log n samples. This provides off-the-shelf, distribution-independent, "amplification" of the amount of data available relative to common-practice estimators. We illustrate the estimator's practical advantages by comparing it to existing estimators for a wide variety of properties and distributions. In most cases, its performance with n samples is even as good as that of the empirical estimator with nlog n samples, and for essentially all properties, its performance is comparable to that of the best existing estimator designed specifically for that property.