Explanations of Black-Box Models based on Directional Feature Interactions
Authors: Aria Masoomi, Davin Hill, Zhonghui Xu, Craig P Hersh, Edwin K. Silverman, Peter J. Castaldi, Stratis Ioannidis, Jennifer Dy
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our bivariate method on Shapley value explanations, and experimentally demonstrate the ability of directional explanations to discover feature interactions. We show the superiority of our method against state-of-the-art on CIFAR10, IMDB, Census, Divorce, Drug, and gene data. |
| Researcher Affiliation | Academia | 1Northeastern University, Department of Electrical and Computer Engineering, Boston, MA, USA. 2Brigham and Women s Hospital, Channing Division of Network Medicine, Boston, MA, USA |
| Pseudocode | Yes | Algorithm 1 Approximate Graph G with Shapley Sampling Algorithm |
| Open Source Code | Yes | All source code is publicly available.3 (Footnote 3: https://github.com/davinhill/Bivariate Shapley) |
| Open Datasets | Yes | We evaluate our methods on COPDGene (Regan et al., 2010), CIFAR10 (Krizhevsky, 2009) and MNIST (Le Cun & Cortes, 2010) image data, IMDB text data, and on three tabular UCI datasets (Drug, Divorce, and Census) (Dua & Graff, 2017). |
| Dataset Splits | No | Table 3 'Summary of the datasets and models in our investigation' provides 'Train/Test Samples' counts (e.g., '1,641/407' for COPD) but does not specify a separate validation split or the methodology for cross-validation. |
| Hardware Specification | Yes | All experiments are performed on an internal cluster with Intel Xeon Gold 6132 CPUs and Nvidia Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions several software packages and libraries such as Network X, Scikit-Network, kernel SHAP, Pytorch Geometric, NLTK, GloVe, Adam, and XGBoost, but it does not specify version numbers for any of them (e.g., 'We use the package Network X (Schult, 2008)'). |
| Experiment Setup | Yes | The paper provides specific experimental setup details for each dataset and model in Section G.1.3. For example, for COPDGene, it states: 'We use a neural network with 4 fully-connected layers of 200 hidden units, batch normalization, and relu activation. The model is trained using Adam (Kingma & Ba, 2017) with learning rate 10 3 for 800 epochs'. |