Regularizing Black-box Models for Improved Interpretability
Authors: Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric Xing, Ameet Talwalkar
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that post-hoc explanations for EXPO-regularized models have better explanation quality, as measured by the common fidelity and stability metrics. We verify that improving these metrics leads to significantly more useful explanations with a user study on a realistic task.4 Experimental Results |
| Researcher Affiliation | Collaboration | Gregory Plumb Carnegie Mellon University EMAIL Maruan Al-Shedivat Carnegie Mellon University EMAIL Ángel Alexander Cabrera Carnegie Mellon University EMAIL Adam Perer Carnegie Mellon University EMAIL Eric Xing CMU, Petuum Inc EMAIL Ameet Talwalkar CMU, Determined AI EMAIL |
| Pseudocode | Yes | Algorithm 1 Neighborhood-fidelity regularizer |
| Open Source Code | Yes | 1https://github.com/GDPlumb/ExpO |
| Open Datasets | Yes | seven regression problems from the UCI collection [Dheeru and Karra Taniskidou, 2017], the MSD dataset4, and Support2 which is an in-hospital mortality classification problem5. Dataset statistics are in Table 2. 4As in [Bloniarz et al., 2016], we treat the MSD dataset as a regression problem with the goal of predicting the release year of a song. 5http://biostat.mc.vanderbilt.edu/wiki/Main/Support Desc. |
| Dataset Splits | No | The paper mentions evaluating on test data and discusses parameters for neighborhoods (Nx and Nreg x), but it does not provide explicit train/validation/test dataset splits (percentages or counts) in the main text. |
| Hardware Specification | Yes | Each model takes less than a few minutes to train on an Intel 8700k CPU, so computational cost was not a limiting factor in our experiments. |
| Software Dependencies | No | The paper mentions using 'SGD with Adam [Kingma and Ba, 2014]' as an optimization algorithm, but it does not specify any software libraries, frameworks, or their version numbers (e.g., Python, TensorFlow, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | The network architectures and hyper-parameters are chosen using a grid search; for more details see Appendix A.3. For the final results, we set Nx to be N(x, σ) with σ = 0.1 and N reg x to be N(x, σ) with σ = 0.5. |