Curvature Enhanced Data Augmentation for Regression
Authors: Ilya Kaufman, Omri Azencot
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations across multiple datasets and comparisons with state-of-the-art methods demonstrate that CEMS delivers superior performance in both in-distribution and out-of-distribution scenarios, while introducing only minimal computational overhead. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel. Correspondence to: Ilya Kaufman <EMAIL>. |
| Pseudocode | Yes | Pseudo-code and illustration of CEMS are given in Alg. 1 and Fig. 2. |
| Open Source Code | Yes | Code is available at https: //github.com/azencot-group/CEMS. |
| Open Datasets | Yes | Airfoil Self-Noise (Airfoil) (Brooks et al., 2014), containing aerodynamic and acoustic measurements of airfoil blade sections, and NO2 (Aldrin, 2004), which predicts air pollution levels at specific locations. We also use two time series datasets: Exchange-Rate and Electricity (Lai et al., 2018)... |
| Dataset Splits | Yes | The dataset is divided into 1003 training examples, 300 validation examples, and 200 test examples as noted in (Hwang & Whang, 2021). |
| Hardware Specification | Yes | The results are obtained with a single RTX3090 GPU. |
| Software Dependencies | No | The paper mentions "Our PyTorch implementation" but does not specify any version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | We present the hyperparameters for each dataset in Table 10. In our main results, we apply our method to the input space or the latent space, and we report the configuration with the best performance. All hyperparameters were selected through cross-validation and evaluated on the validation set. |