Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory
Authors: Pasan Dissanayake, Sanghamitra Dutta
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our strategy improves fidelity between the target and surrogate model predictions on several datasets. and We conduct experiments on both synthetic datasets, as well as, four real-world datasets, namely, Adult Income [Becker and Kohavi, 1996], COMPAS [Angwin et al., 2016], DCCC [Yeh, 2016], and HELOC [FICO, 2018]. |
| Researcher Affiliation | Academia | Pasan Dissanayake University of Maryland College Park, MD pasand@umd.edu Sanghamitra Dutta University of Maryland College Park, MD sanghamd@umd.edu |
| Pseudocode | Yes | Algorithm 1 Counterfactual Clamping |
| Open Source Code | Yes | A python-based implementation is available at: https://github.com/pasandissanayake/ model-reconstruction-using-counterfactuals. |
| Open Datasets | Yes | We use four publicly available real-world tabular datasets (namely, Adult Income, COMPAS, DCCC, and HELOC) to evaluate the performance of the attack proposed in Section 3.3. and Adult Income: The dataset is a 1994 census database with information such as educational level, marital status, age and annual income of individuals [Becker and Kohavi, 1996]. |
| Dataset Splits | No | No explicit training/validation/test split percentages or sample counts for a separate validation set are provided for the primary model training or evaluation, beyond mentioning 'test-train-attack split' and using 'Dref' for fidelity evaluation. |
| Hardware Specification | Yes | All the experiments were carried-out on two computers, one with a NVIDIA RTX A4500 GPU and the other with a NVIDIA RTX 3050 Mobile. |
| Software Dependencies | No | The paper mentions a 'python-based implementation' but does not specify particular software versions for libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | The hidden layer activations are Re LU and the layer weights are L2 regularized. The regularization coefficient is 0.001. Each model is trained for 200 epochs, with a batch size of 32. |