Continuous Invariance Learning
Authors: LIN Yong, Fan Zhou, Lu Tan, Lintao Ma, Jianmeng Liu, Yansu HE, Yuan Yuan, Yu Liu, James Y. Zhang, Yujiu Yang, Hao Wang
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on both synthetic and real-world datasets (including data collected from production systems) show that CIL consistently outperforms strong baselines among all the tasks. |
| Researcher Affiliation | Collaboration | 1The Hong Kong University of Science and Technology, 2Ant Group, 3Rutgers University, 4Tsinghua University, 5Chinese University of Hong Kong, 6MIT CSAIL, 7Boston College |
| Pseudocode | Yes | Algorithm 1 CIL: Continuous Invariance Learning |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository. |
| Open Datasets | Yes | We conduct experiments on CMNIST (Arjovsky et al., 2019); House Price dataset from Kaggle; Insurance Fraud dataset on Kaggle; Wildtime-Yearbook (Yao et al., 2022). |
| Dataset Splits | Yes | The dataset is partitioned according to the built year, with the training dataset in the period [1900, 1950] and the test dataset in the period (1950, 2000]. |
| Hardware Specification | Yes | All experiments are done on a server base on Alibaba Group Enterprise Linux Server release 7.2 (Paladin) system which has 2 GP100GL [Tesla P100 PCIe 16GB] GPU devices. |
| Software Dependencies | No | The paper specifies experimental parameters such as learning rates and epochs, but it does not list specific software dependencies with their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In this section, we provide the training and hyperparameter details for the experiments. All experiments are done on a server base on Alibaba Group Enterprise Linux Server release 7.2 (Paladin) system which has 2 GP100GL [Tesla P100 PCIe 16GB] GPU devices. LR: learning rate of the classification model Φ(x)), e.g. 1e-3. OLR: learning rate of the penalty model h(Φ(x)), g(Φ(x), y), e.g. 0.001. Steps: total number of epochs for the training process, e.g. 1500. Penalty Step: number of epochs when to introduce penalty, e.g. 500. Penalty Weight: the invariance penalty weight, e.g. 1000. We show the parameter values used for each dataset in Table 11. |