Invariant Learning via Probability of Sufficient and Necessary Causes
Authors: Mengyue Yang, Zhen Fang, Yonggang Zhang, Yali Du, Furui Liu, Jean-Francois Ton, Jianhong Wang, Jun Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world benchmarks demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1University College London, 2University of Technology Sydney 3Hong Kong Baptist University, 4King s College London 5Zhejiang Lab, 6Byte Dance Research, 7University of Manchester |
| Pseudocode | No | The paper describes the proposed algorithm (Ca SN) and its optimization process in prose, but does not provide a formal pseudocode block or algorithm box. |
| Open Source Code | Yes | The detailed implementation can be found at the Git Hub repository: https://github.com/ymy4323460/Ca SN. |
| Open Datasets | Yes | We test the performance on commonly used Colored Mnist (Ahuja et al., 2020a), PACS (Li et al., 2017), and VLCS (Fang et al., 2013) datasets. |
| Dataset Splits | Yes | The training dataset is randomly split as training and validation datasets, the hyperparameters are selected on the validation dataset, which maximizes the performance of the validation dataset. |
| Hardware Specification | Yes | All the experiments are conducted based on a server with a 16-core CPU, 128g memory and RTX 5000 GPU. |
| Software Dependencies | No | The paper mentions using "Domain Bed" codebase and specific network architectures like "Res Net-50" and "SGD as the optimizer", but does not provide specific version numbers for these software components or programming languages. |
| Experiment Setup | Yes | The Hyperparameters are shown in Table 3. The general hyperparameters (e.g. Res Net, Mnist, Not Minist) are directly given from Table 8 in Gulrajani & Lopez-Paz (2020). All the experiments run for 2 times. |