Explainable Neural Networks with Guarantee: A Sparse Estimation Approach
Authors: Antoine Ledent, Peng Liu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the experimental results on both synthetic and real data. We allocate 20% of the data to the test set in all experiments, with the hyperparameters tuned by cross-validation. Our evaluation of the proposed algorithm serves a dual purpose: first, to gauge its predictive power, and second, to assess its capability to retrieve the sparse set of true features accurately. |
| Researcher Affiliation | Academia | Antoine Ledent and Peng Liu* Singapore Management University EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in prose and mathematical equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to a source code repository, nor does it explicitly state that the code for the methodology described is being released or available in supplementary materials. |
| Open Datasets | Yes | Finally, we evaluate Spar Xnet on six real-life datasets, including adult income, breast cancer, credit risk, customer churn, heart disease, and recidivism. |
| Dataset Splits | Yes | We allocate 20% of the data to the test set in all experiments, with the hyperparameters tuned by cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., libraries, frameworks, or programming languages with their versions) used in the experiments. |
| Experiment Setup | Yes | We use one pathway with six fully connected layers to learn the underlying data-generating process and identify the true feature. Each hidden layer consists of 128 nodes, followed by a dropout layer. We use Bayesian optimization to optimize three hyperparameters: dropout rate (between 0.1 and 0.5), learning rate (between 0.001 and 0.01), and temperature (between 0.1 and 100). The temperature is then slowly reduced to 1% of its initial value throughout a total training budget of 2000 iterations. |