# Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching

arXiv: https://arxiv.org/abs/1907.03426 · HTML: https://mlsanity.com/paper/1907.03426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

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

- [Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions](https://mlsanity.com/paper/2211.00568/) (2022)
- [Saliency Diversified Deep Ensemble for Robustness to Adversaries](https://mlsanity.com/paper/2112.03615/) (2021)

## Abstract (arXiv)

A broad range of cross-m-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as m increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i.e., MMI-ALI for m-domain joint distribution matching. As an m-domain ensemble model of ALIs citedumoulin2016adversarially, MMI-ALI is adversarially trained with maximizing Multivariate Mutual Information (MMI) w.r.t. joint variables of each pair of domains and their shared feature. The negative MMIs are upper bounded by a series of feasible losses that provably lead to matching m-domain joint distributions. MMI-ALI linearly scales as m increases and thus, strikes a right balance between efficacy and scalability. We evaluate MMI-ALI in diverse challenging m-domain scenarios and verify its superiority.