# Newfluence: Boosting Model interpretability and Understanding in High Dimensions

arXiv: https://arxiv.org/abs/2507.11895 · HTML: https://mlsanity.com/paper/2507.11895/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Estimating Training Data Influence by Tracing Gradient Descent](https://mlsanity.com/paper/2002.08484/) (2020)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Studying Large Language Model Generalization with Influence Functions](https://mlsanity.com/paper/2308.03296/) (2023)
- [On the (In)fidelity and Sensitivity for Explanations](https://mlsanity.com/paper/1901.09392/) (2019)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Datamodels: Predicting Predictions from Training Data](https://mlsanity.com/paper/2202.00622/) (2022)
- [Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2110.14049/) (2021)
- [Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions](https://mlsanity.com/paper/2005.06676/) (2020)
- [If Influence Functions are the Answer, Then What is the Question?](https://mlsanity.com/paper/2209.05364/) (2022)
- [Training Data Influence Analysis and Estimation: A Survey](https://mlsanity.com/paper/2212.04612/) (2022)
- [The Shapley Value in Machine Learning](https://mlsanity.com/paper/2202.05594/) (2022)
- [Data Shapley in One Training Run](https://mlsanity.com/paper/2406.11011/) (2024)
- [Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs](https://mlsanity.com/paper/2303.08114/) (2023)
- [Capturing the Temporal Dependence of Training Data Influence](https://mlsanity.com/paper/2412.09538/) (2024)
- [Theoretical and Practical Perspectives on what Influence Functions Do](https://mlsanity.com/paper/2305.16971/) (2023)
- [The Likelihood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Square](https://mlsanity.com/paper/1706.01191/) (2017)
- [Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions](https://mlsanity.com/paper/1807.02694/) (2018)
- [Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions](https://mlsanity.com/paper/2003.01770/) (2020)
- [Understanding Impact of Human Feedback via Influence Functions](https://mlsanity.com/paper/2501.05790/) (2025)
- [Correcting Large Language Model Behavior via Influence Function](https://mlsanity.com/paper/2412.16451/) (2024)
- [Certified Data Removal Under High-dimensional Settings](https://mlsanity.com/paper/2505.07640/) (2025)

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

- [Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions](https://mlsanity.com/paper/2509.15950/) (2025)
- [Market Design for AI: Beyond the Copyright Binary](https://mlsanity.com/paper/2606.12260/) (2026)

## Abstract (arXiv)

The increasing complexity of machine learning (ML) and artificial intelligence (AI) models has created a pressing need for tools that help scientists, engineers, and policymakers interpret and refine model decisions and predictions. Influence functions, originating from robust statistics, have emerged as a popular approach for this purpose. However, the heuristic foundations of influence functions rely on low-dimensional assumptions where the number of parameters p is much smaller than the number of observations n. In contrast, modern AI models often operate in high-dimensional regimes with large p, challenging these assumptions. In this paper, we examine the accuracy of influence functions in high-dimensional settings. Our theoretical and empirical analyses reveal that influence functions cannot reliably fulfill their intended purpose. We then introduce an alternative approximation, called Newfluence, that maintains similar computational efficiency while offering significantly improved accuracy. Newfluence is expected to provide more accurate insights than many existing methods for interpreting complex AI models and diagnosing their issues. Moreover, the high-dimensional framework we develop in this paper can also be applied to analyze other popular techniques, such as Shapley values.