Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Authors: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically validate our theory using extensive experimentation on both synthetic and real world datasets. |
| Researcher Affiliation | Academia | 1David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada 2Department of Computer Science, Harvard University, Cambridge, MA, USA 3School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided or link to a repository. |
| Open Datasets | Yes | We generate a synthetic dataset and use 2 real world classification datasets from the UCI Machine Learning Repository (Dua & Graff, 2017). |
| Dataset Splits | Yes | We follow the standard 80/20 dataset split, i.e., 80% of the data was used for training the model while 20% was used for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'ELU activation function' but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The models are trained using Adam optimizer using a cross-entropy loss function. Our best performing models achieve a testing accuracy of 99.50%, 96.30%, and 99.8% using 15, 60, and 100 training epochs for the Simulated, Bankruptcy, and Online Shopping datasets, respectively. We also train models using fewer than the aforementioned training epochs to assess the the impact of model accuracy on our equivalence and robustness guarantees. Consistent with our theory, for any input point x, for both C-LIME and Smooth Grad we generate perturbations from a local neighborhood of x by sampling points from N(x, σ2I). We study the effect of the number of perturbations and the value of σ2 in our experiments. |