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..
The Many Shapley Values for Model Explanation
Authors: Mukund Sundararajan, Amir Najmi
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While the bulk of this paper is axiomatic and theoretical, we will replicate some of our observations on a diabetes prediction task...We train a linear model using Scikit s implementation of Lasso regression (Tibshirani, 1996); we used the standard settings of the fitting algorithm and 75%-25% train-test split. The variance explained by the model is 35%. Figure 1 shows the distribution of attributions across 20 explicands for CES( ˆD)... |
| Researcher Affiliation | Industry | Mukund Sundararajan 1 Amir Najmi 1, 1Google LLC. Correspondence to: Mukund Sundararajan <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Computing CES( ˆD) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for the described methodology. |
| Open Datasets | Yes | We train our diabetes prediction models on a data set from the Scikit learning library (Pedregosa et al., 2011); this data set was originally used in (Efron et al., 2004). |
| Dataset Splits | No | The paper mentions a "75%-25% train-test split" but does not explicitly describe a separate validation split or how validation was performed. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Scikit learning library" for training and "Scikit s implementation of Lasso regression" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper states "we used the standard settings of the fitting algorithm" but does not provide specific details about hyperparameters, model initialization, or other training configurations. |