Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
First-Order Manifold Data Augmentation for Regression Learning
Authors: Ilya Kaufman, Omri Azencot
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate FOMA on in-distribution generalization and out-of-distribution robustness benchmarks, and we show that it improves the generalization of several neural architectures. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel. |
| Pseudocode | Yes | We provide an example Py Torch pseudocode in Fig. 1 (left). |
| Open Source Code | Yes | Our code is publicly available at https://github.com/ azencot-group/FOMA |
| Open Datasets | Yes | We use the following five datasets to evaluate the performance of in-distribution generalization. Two tabular datasets: Airfoil Self-Noise (Airfoil) (Brooks et al., 2014) and NO2 (Aldrin, 2004). Two time series datasets: Exchange-Rate and Electricity (Lai et al., 2018)... |
| Dataset Splits | Yes | As per reference (Hwang & Whang, 2021), the training, validation, and test sets consist of 1003, 300, and 200 examples, respectively. (Airfoil dataset) |
| Hardware Specification | Yes | The results are obtained with a single RTX3090 GPU. |
| Software Dependencies | No | The paper uses PyTorch as implied by the pseudocode (torch.linalg.svd), but no specific version numbers for PyTorch or other software dependencies are provided in the text. |
| Experiment Setup | Yes | Detailed experimental settings and hyperparameters are provided in App. E. We list the hyperparameters for every dataset in Table 8 and Table 9 for the methods FOMA and FOMAρ, respectively. |