# Chernoff Sampling for Active Testing and Extension to Active Regression

arXiv: https://arxiv.org/abs/2012.08073 · HTML: https://mlsanity.com/paper/2012.08073/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 13 of 13)

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- [Adaptive Partitioning Schemes for Optimistic Optimization](https://mlsanity.com/paper/2610.04039/) (2026)
- [Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback](https://mlsanity.com/paper/2610.17760/) (2026)

## Abstract (arXiv)

Active learning can reduce the number of samples needed to perform a hypothesis test and to estimate the parameters of a model. In this paper, we revisit the work of Chernoff that described an asymptotically optimal algorithm for performing a hypothesis test. We obtain a novel sample complexity bound for Chernoff's algorithm, with a non-asymptotic term that characterizes its performance at a fixed confidence level. We also develop an extension of Chernoff sampling that can be used to estimate the parameters of a wide variety of models and we obtain a non-asymptotic bound on the estimation error. We apply our extension of Chernoff sampling to actively learn neural network models and to estimate parameters in real-data linear and non-linear regression problems, where our approach performs favorably to state-of-the-art methods.