Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
Authors: Yujia Bao, Shiyu Chang, Regina Barzilay
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that our algorithm is able to learn robust classifiers (outperforms IRM by 23.85% on synthetic environments and 12.41% on natural environments). We evaluate our method on both text classification and image classification. |
| Researcher Affiliation | Collaboration | Yujia Bao 1 Shiyu Chang 2 Regina Barzilay 3 1MIT CSAIL 2MIT-IBM Watson AI Lab 3MIT CSAIL. |
| Pseudocode | No | The paper describes the algorithm in Section 3.1 using numbered stages and text, but it does not provide a formally labeled pseudocode block or algorithm figure. |
| Open Source Code | Yes | Our code and data are available at https://github.com/YujiaBao/ Predict-then-Interpolate. |
| Open Datasets | Yes | For MNIST, we adopt Arjovsky et al. (2019) s approach for generating spurious correlation and extend it to a more challenging multi-class problem. For Beer Review, we consider three aspect-level sentiment classification tasks: look, aroma and palate (Lei et al., 2016; Bao et al., 2018). We study two datasets: Celeb A (Liu et al., 2015b) where the attributes are annotated by human and ASK2ME (Bao et al., 2019) where the attributes are automatically generated by rules. |
| Dataset Splits | Yes | For both datasets, we consider two different validation settings and report their performance separately: 1) sampling the validation set from the training environment; 2) sampling the validation set from the testing environment. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud instance specifications used for running the experiments. |
| Software Dependencies | No | The paper states 'Our implementation builds on PyTorch (Paszke et al., 2019) and Adam optimizer (Kingma & Ba, 2014).', but it does not specify version numbers for PyTorch or any other software libraries. |
| Experiment Setup | Yes | For all models, we use the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 1e-3 and a batch size of 128. We train the models for 100 epochs. |