Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |