SAND: One-Shot Feature Selection with Additive Noise Distortion
Authors: Pedram Pad, Hadi Hammoud, Mohamad Dia, Nadim Maamari, Liza Andrea Dunbar
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an extensive benchmarking study against state-of-the-art feature selection methods using common datasets and a novel real-world dataset, showcasing our method s effective competition against existing approaches. . . . Test metrics on 9 datasets over 10 trials. The metric is accuracy ( ) for all except MAE ( ) for CA Housing being a regression problem. |
| Researcher Affiliation | Collaboration | 1CSEM, Neuchâtel, Switzerland 2EPFL, Lausanne, Switzerland. Correspondence to: Pedram Pad <EMAIL>. |
| Pseudocode | No | The paper describes the mathematical model of the SAND layer using equations (1), (2), and (3) and explains its mechanism in paragraph text, but it does not contain a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | The code to reproduce our experiments is available at https://github.com/csem/SAND |
| Open Datasets | Yes | Specifically, we utilized nine datasets, seven of which were used in previous studies by (Balın et al., 2019; Lemhadri et al., 2021; Yamada et al., 2020; Yasuda et al., 2023). The additional real and synthetic datasets were California Housing (Torgo, 1997) and HAR70 (Logacjov & Ustad, 2023). . . . The MSI Grain dataset. . . is available in the code repository at https://github.com/csem/SAND. |
| Dataset Splits | Yes | Across all experiments, we employed the Adam optimizer with a learning rate of 10 3, and we partitioned the datasets into 70-10-20 splits for training, validation, and testing, respectively. |
| Hardware Specification | Yes | Moreover, the experiments were executed on a machine equipped with an NVIDIA Ge Force RTX 4090 GPU with 24GB of RAM, paired with an AMD Ryzen 9 5900X 12-Core Processor featuring 24 threads. |
| Software Dependencies | No | The paper mentions the 'Adam optimizer' but does not specify version numbers for any software libraries, programming languages, or other dependencies required to replicate the experiment. |
| Experiment Setup | Yes | Across all experiments, we employed the Adam optimizer with a learning rate of 10 3. . . . For hyperparameters of the SAND layer, we used σ = 1.5 and α = 2 consistently. Unless otherwise specified, we selected k = 60 features for all datasets by default, except for the following: k = 5 for the Madelon dataset, k = 3 for the CA Housing dataset, and k = 6 for the Har70 dataset. . . . Table 3 containing details about all datasets utilized in the feature selection experiments. Additionally, the table includes the epochs employed during training for each dataset. . . and batch size used for training. |