# Equivalence Testing: The Power of Bounded Adaptivity

arXiv: https://arxiv.org/abs/2403.04230 · HTML: https://mlsanity.com/paper/2403.04230/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Monotonicity Testing of High-Dimensional Distributions with Subcube Conditioning](https://mlsanity.com/paper/2502.16355/) (2025)
- [Hypothesis Testing with Conditional Queries: Learnability and the Value of Interaction](https://mlsanity.com/paper/2608.06262/) (2026)

## Abstract (arXiv)

Equivalence testing, a fundamental problem in the field of distribution testing, seeks to infer if two unknown distributions on [n] are the same or far apart in the total variation distance. Conditional sampling has emerged as a powerful query model and has been investigated by theoreticians and practitioners alike, leading to the design of optimal algorithms albeit in a sequential setting (also referred to as adaptive tester). Given the profound impact of parallel computing over the past decades, there has been a strong desire to design algorithms that enable high parallelization. Despite significant algorithmic advancements over the last decade, parallelizable techniques (also termed non-adaptive testers) have tildeO(log12n) query complexity, a prohibitively large complexity to be of practical usage. Therefore, the primary challenge is whether it is possible to design algorithms that enable high parallelization while achieving efficient query complexity. Our work provides an affirmative answer to the aforementioned challenge: we present a highly parallelizable tester with a query complexity of tildeO(log n), achieved through a single round of adaptivity, marking a significant stride towards harmonizing parallelizability and efficiency in equivalence testing.