Stability Guarantees for Feature Attributions with Multiplicative Smoothing
Authors: Anton Xue, Rajeev Alur, Eric Wong
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Mu S on vision and language models with various feature attribution methods, such as LIME and SHAP, and demonstrate that Mu S endows feature attributions with non-trivial stability guarantees. |
| Researcher Affiliation | Academia | Anton Xue Rajeev Alur Eric Wong Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 {antonxue,alur,exwong}@seas.upenn.edu |
| Pseudocode | No | The paper describes algorithms in text and figures, but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for their methodology or a direct link to a code repository. |
| Open Datasets | Yes | We use Image Net1K [31] as our vision dataset and Tweet Eval [32] sentiment analysis as our language dataset. |
| Dataset Splits | No | The paper mentions training, but does not explicitly state dataset splits (e.g., 80/10/10) for training, validation, and testing. |
| Hardware Specification | No | The paper mentions using specific models like Vision Transformer and ResNet50, but does not provide details on the hardware (e.g., specific GPUs, CPUs) used for experiments. |
| Software Dependencies | No | The paper mentions using Adam [61] as an optimizer, but does not provide version numbers for any software dependencies. |
| Experiment Setup | Yes | Training Details We used Adam [61] as our optimizer with default parameters and a learning rate of 10-6 for 5 epochs. Because we consider λ {1/8, 2/8, 3/8, 4/8, 8/8} and h {Vision Transformer, Res Net50, Ro BERTa}, there are a total of 15 different models for most experiments. To train with a particular λ: for each training input x, we generate two random maskings one where λ of the features are zeroed and one where λ/2 of the features are zeroed. This additional λ/2 zeroing is to account for the fact that inputs to a smoothed model will be subject to masking by λ as well as φ(x), where the scaling factor of 1/2 is informed by our prior experience about the size of a stable explanation. |