Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
GEFA: A General Feature Attribution Framework Using Proxy Gradient Estimation
Authors: Yi Cai, Thibaud Ardoin, Gerhard Wunder
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared to traditional sampling-based Shapley Value estimators, GEFA avoids potential information waste sourced from computing marginal contributions, thereby improving explanation quality, as demonstrated in quantitative evaluations across various settings.5. Experiments |
| Researcher Affiliation | Academia | 1Department of Mathematics and Computer Science, Freie Universit at Berlin, Berlin, Germany. Correspondence to: Yi Cai <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 GEFA Explanation Scheme Algorithm 2 Smoothing Enhanced Mask Sampling |
| Open Source Code | Yes | 2Code is available at: https://github.com/caiy0220/GEFA |
| Open Datasets | Yes | Three datasets are adopted for text classification tasks: Amazon Review Polarity (Mc Auley & Leskovec, 2013), STS-2, and QNLI (Wang et al., 2019). The image classification task is set up with Image Net (Russakovsky et al., 2015) |
| Dataset Splits | Yes | Without losing generality, we adopted a lightweight model for image classification and downsampled the dataset into 2000/400/400 partitions for training, validation, and test sets to ensure feasibility and efficiency. |
| Hardware Specification | Yes | Processor: Intel i9-10980XE, 18 cores Memory: 32GB DDR4 GPU: NVIDIA RTX A5500, 24GB |
| Software Dependencies | Yes | The primary packages were Numpy 1.26.4, Py Torch of version 2.5.0, and Torchvision 0.20.0. The CUDA version was 12.2 for GPU support. |
| Experiment Setup | Yes | For all test cases, the query budget for the black-box explainers is 500, given the relatively smaller feature space; the interpolation step for IG is set to 50. The query budget of the black-box approaches is increased to 5000 due to the considerably larger input feature spaces, which are 299 299 and 224 224 for Inception V3 and Vi T, respectively. |