# Informative Features for Model Comparison

arXiv: https://arxiv.org/abs/1810.11630 · HTML: https://mlsanity.com/paper/1810.11630/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [KSD Aggregated Goodness-of-fit Test](https://mlsanity.com/paper/2202.00824/) (2022)
- [Composite Goodness-of-fit Tests with Kernels](https://mlsanity.com/paper/2111.10275/) (2021)
- [A Stein Goodness of fit Test for Exponential Random Graph Models](https://mlsanity.com/paper/2103.00580/) (2021)
- [Kernel-Based Tests for Likelihood-Free Hypothesis Testing](https://mlsanity.com/paper/2308.09043/) (2023)
- [Kernel Tests of Equivalence](https://mlsanity.com/paper/2603.10886/) (2026)
- [A Kernelised Stein Statistic for Assessing Implicit Generative Models](https://mlsanity.com/paper/2206.00149/) (2022)
- [Anchor-based Maximum Discrepancy for Relative Similarity Testing](https://mlsanity.com/paper/2510.10477/) (2025)
- [Monitoring AI systems: A Problem Analysis, Framework and Outlook](https://mlsanity.com/paper/2205.02562/) (2022)
- [Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings](https://mlsanity.com/paper/2310.10414/) (2023)

## Abstract (arXiv)

Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models. We propose two new statistical tests which are nonparametric, computationally efficient (runtime complexity is linear in the sample size), and interpretable. As a unique advantage, our tests can produce a set of examples (informative features) indicating the regions in the data domain where one model fits significantly better than the other. In a real-world problem of comparing GAN models, the test power of our new test matches that of the state-of-the-art test of relative goodness of fit, while being one order of magnitude faster.