midr: Learning from Black-Box Models by Maximum Interpretation Decomposition

• URL: http://arxiv.org/abs/2506.08338v1
• Date: Tue, 10 Jun 2025 01:46:49 GMT
• Title: midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
• Authors: Ryoichi Asashiba, Reiji Kozuma, Hirokazu Iwasawa,
• Abstract summary: We introduce the R package midr, which implements Maximum Decomposition (MID)<n>MID is a functional decomposition approach that derives a low-order additive representation of a black-box model.<n>midr enables learning from black-box models by constructing a global surrogate model with advanced analytical capabilities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of appropriate methods of Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI) is essential for adopting black-box predictive models in fields where model and prediction explainability is required. As a novel tool for interpreting black-box models, we introduce the R package midr, which implements Maximum Interpretation Decomposition (MID). MID is a functional decomposition approach that derives a low-order additive representation of a black-box model by minimizing the squared error between the model's prediction function and this additive representation. midr enables learning from black-box models by constructing a global surrogate model with advanced analytical capabilities. After reviewing related work and the theoretical foundation of MID, we demonstrate the package's usage and discuss some of its key features.

Related papers

• Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors [61.92704516732144]
  We show that the most robust features for correctness prediction are those that play a distinctive causal role in the model's behavior.<n>We propose two methods that leverage causal mechanisms to predict the correctness of model outputs.
  arXiv  Detail & Related papers  (2025-05-17T00:31:39Z)
• Predicting the Performance of Black-box LLMs through Self-Queries [60.87193950962585]
  Large language models (LLMs) are increasingly relied on in AI systems, predicting when they make mistakes is crucial.<n>In this paper, we extract features of LLMs in a black-box manner by using follow-up prompts and taking the probabilities of different responses as representations.<n>We demonstrate that training a linear model on these low-dimensional representations produces reliable predictors of model performance at the instance level.
  arXiv  Detail & Related papers  (2025-01-02T22:26:54Z)
• On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning [85.75164588939185]
  We study the discriminative probabilistic modeling on a continuous domain for the data prediction task of (multimodal) self-supervised representation learning.<n>We conduct generalization error analysis to reveal the limitation of current InfoNCE-based contrastive loss for self-supervised representation learning.<n>We propose a novel non-parametric method for approximating the sum of conditional probability densities required by MIS.
  arXiv  Detail & Related papers  (2024-10-11T18:02:46Z)
• Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects [4.3500439062103435]
  We propose a novel approach for the functional decomposition of black-box predictions. Similar to additive regression models, our method provides insights into the direction and strength of the main feature contributions.
  arXiv  Detail & Related papers  (2024-07-26T10:37:29Z)
• Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set [0.0]
  Interpretability of machine learning models plays a role as important as the model accuracy. This paper proposes a benchmark scheme to evaluate explanatory methods to explain regression models. Results have shown that Symbolic Regression models can be an interesting alternative to white-box and black-box models.
  arXiv  Detail & Related papers  (2024-04-08T23:46:59Z)
• GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints [5.783415024516947]
  This paper investigates a series of intrinsically interpretable machine learning models. We evaluate the prediction qualities of five GAMs as compared to six traditional ML models.
  arXiv  Detail & Related papers  (2022-04-19T20:37:31Z)
• Hessian-based toolbox for reliable and interpretable machine learning in physics [58.720142291102135]
  We present a toolbox for interpretability and reliability, extrapolation of the model architecture. It provides a notion of the influence of the input data on the prediction at a given test point, an estimation of the uncertainty of the model predictions, and an agnostic score for the model predictions. Our work opens the road to the systematic use of interpretability and reliability methods in ML applied to physics and, more generally, science.
  arXiv  Detail & Related papers  (2021-08-04T16:32:59Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning [5.479705009242287]
  We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model. We design an iterative training algorithm to jointly train the two types of models. Experimental results show that PIE is highly competitive to black-box models while outperforming interpretable baselines.
  arXiv  Detail & Related papers  (2021-05-06T03:06:34Z)
• Design of Dynamic Experiments for Black-Box Model Discrimination [72.2414939419588]
  Consider a dynamic model discrimination setting where we wish to chose: (i) what is the best mechanistic, time-varying model and (ii) what are the best model parameter estimates. For rival mechanistic models where we have access to gradient information, we extend existing methods to incorporate a wider range of problem uncertainty. We replace these black-box models with Gaussian process surrogate models and thereby extend the model discrimination setting to additionally incorporate rival black-box model.
  arXiv  Detail & Related papers  (2021-02-07T11:34:39Z)
• A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution [3.3758186776249928]
  We aim to address this problem in settings where the predictive model is a black box. We reduce the problem of interpreting a black box predictive model to that of estimating the causal effects of each of the model inputs on the model output. We show how the resulting causal attribution of responsibility for model output to the different model inputs can be used to interpret the predictive model and to explain its predictions.
  arXiv  Detail & Related papers  (2020-08-01T23:20:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.