# A simple polynomial-time approximation algorithm for the total variation distance between two product distributions

arXiv: https://arxiv.org/abs/2208.00740 · HTML: https://mlsanity.com/paper/2208.00740/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2022.

## Built on by (most cited first, 8 of 8)

- [Total Variation Distance Meets Probabilistic Inference](https://mlsanity.com/paper/2309.09134/) (2023)
- [Sharp bounds on aggregate expert error](https://mlsanity.com/paper/2407.16642/) (2024)
- [Bias Detection via Maximum Subgroup Discrepancy](https://mlsanity.com/paper/2502.02221/) (2025)
- [Approximating the total variation distance between spin systems](https://mlsanity.com/paper/2502.05437/) (2025)
- [Approximating the Total Variation Distance between Gaussians](https://mlsanity.com/paper/2503.11099/) (2025)
- [On approximating the f-divergence between two Ising models](https://mlsanity.com/paper/2509.05016/) (2025)
- [New Algorithmic Directions in Optimal Transport and Applications for Product Spaces](https://mlsanity.com/paper/2509.21502/) (2025)
- [On Computing Total Variation Distance Between Mixtures of Product Distributions](https://mlsanity.com/paper/2605.03839/) (2026)

## Abstract (arXiv)

We give a simple polynomial-time approximation algorithm for the total variation distance between two product distributions.