Papers with mixing strategy
Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization (2023.findings-acl)
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| Challenge: | Existing methods for minimizing the worst-case loss of annotated groups are lacking in practice due to expensive annotations and privacy issues. |
| Approach: | They propose a distributionally robust optimization framework that relaxes group identification into direct parameterization by using an interactive training mode. |
| Outcome: | The proposed method outperforms state-of-the-art methods on synthetic and real-world text classification tasks. |
Dynamic Nonlinear Mixup with Distance-based Sample Selection (2022.coling-1)
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| Challenge: | Existing methods to augment data with mixup are limited by the space of synthetic data and its regularization effect. |
| Approach: | They propose a dynamic nonlinear mixup with distance-based sample selection which generates multiple sample pairs based on the distance between each sample. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple public datasets. |