Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Authors: Patrick Schwab, Walter Karlen
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,195 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,792 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments that demonstrate that CXPlain is significantly more accurate and faster than existing model-agnostic methods for estimating feature importance. |
| Researcher Affiliation | Academia | Patrick Schwab and Walter Karlen Institute of Robotics and Intelligent Systems, ETH Zurich EMAIL |
| Pseudocode | Yes | We repeat this process M times to obtain a bootstrap ensemble of M explanation models (Algorithm in Appendix B). |
| Open Source Code | Yes | Source code is available at https://github.com/d909b/cxplain. |
| Open Datasets | Yes | To compare the accuracy of CXPlain to existing state-of-the-art methods for feature importance estimation, we evaluated its ability to identify important features in MNIST [51] and Image Net [52] images. |
| Dataset Splits | No | The paper references using MNIST and Image Net test sets, and discusses training on subsets via bootstrap resampling, but it does not specify explicit training/validation/test dataset splits with percentages or counts in the main text. |
| Hardware Specification | Yes | We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. |
| Software Dependencies | No | The paper mentions TensorFlow [54] but does not provide specific version numbers for it or any other key software dependencies used in the experiments. |
| Experiment Setup | No | As a preprocessing step, pixel values were scaled to be in the range of [0, 1] prior to training. We then used several importance estimation methods to determine which input pixels were most important for the classification models decisions on N = 100 test images. We masked the top 10 and 30% of those most important pixels for MNIST and Image Net, respectively, and measured the resulting change in the classification models confidences by computing the difference in log odds. Further training details are given in Appendix A. |