C-Mixup: Improving Generalization in Regression
Authors: Huaxiu Yao, Yiping Wang, Linjun Zhang, James Y. Zou, Chelsea Finn
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate C-Mixup on eleven datasets, ranging from tabular to video data. Compared to the best prior approach, C-Mixup achieves 6.56%, 4.76%, 5.82% improvements in in-distribution generalization, task generalization, and out-of-distribution robustness, respectively. |
| Researcher Affiliation | Academia | 1Stanford University, 2Zhejiang University, 3Rutgers University |
| Pseudocode | Yes | Algorithm 1 Training with C-Mixup |
| Open Source Code | Yes | Code is released at https://github.com/huaxiuyao/C-Mixup. |
| Open Datasets | Yes | We use the following five datasets to evaluate the performance of in-distribution generalization (see Appendix C.1 for detailed data statistics). (1)&(2) Airfoil Self-Noise (Airfoil) and NO2 [35] are both are tabular datasets... (3)&(4): Exchange-Rate, and Electricity [40] are two time-series datasets... (5) Echocardiogram Videos (Echo) [50] is a ejection fraction prediction dataset... |
| Dataset Splits | Yes | In our experiments, we apply cross-validation to tune all hyperparameters with grid search. ... For Airfoil and NO2, we split the data into 80% training and 20% test sets, without a validation set, as common practice for these datasets. ... For Echo, we use the default training/validation/testing split provided by the dataset. |
| Hardware Specification | Yes | We train all models on a single Nvidia Tesla A100 GPU. |
| Software Dependencies | No | The paper mentions various models and architectures but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For all models, we use the Adam optimizer [40] with a learning rate of 1e-3 and a batch size of 256. We train for 200 epochs, and apply early stopping with patience 20. For mixup variants, we set α = 1.0. For C-Mixup, we set σ = 0.5. |