# Near-Optimal Confidence Sequences for Bounded Random Variables

arXiv: https://arxiv.org/abs/2006.05022 · HTML: https://mlsanity.com/paper/2006.05022/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

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

- [Bregman Deviations of Generic Exponential Families](https://mlsanity.com/paper/2201.07306/) (2022)
- [Nonparametric extensions of randomized response for private confidence sets](https://mlsanity.com/paper/2202.08728/) (2022)
- [Variance-Aware Estimation of Kernel Mean Embedding](https://mlsanity.com/paper/2210.06672/) (2022)
- [Sample size planning for conditional counterfactual mean estimation with a K-armed randomized experiment](https://mlsanity.com/paper/2403.04039/) (2024)

## Abstract (arXiv)

Many inference problems, such as sequential decision problems like A/B testing, adaptive sampling schemes like bandit selection, are often online in nature. The fundamental problem for online inference is to provide a sequence of confidence intervals that are valid uniformly over the growing-into-infinity sample sizes. To address this question, we provide a near-optimal confidence sequence for bounded random variables by utilizing Bentkus' concentration results. We show that it improves on the existing approaches that use the Cram'er-Chernoff technique such as the Hoeffding, Bernstein, and Bennett inequalities. The resulting confidence sequence is confirmed to be favorable in both synthetic coverage problems and an application to adaptive stopping algorithms.