Towards a Theoretical Framework of Out-of-Distribution Generalization
Authors: Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, Liwei Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark OOD datasets demonstrate that our model selection criterion has a significant advantage over baselines. |
| Researcher Affiliation | Collaboration | Haotian Ye Peking University Pazhou Lab haotianye@pku.edu.cn Chuanlong Xie Huawei Noah s Ark Lab xie.chuanlong@huawei.com Tianle Cai Peking University caitianle1998@pku.edu.cn Ruichen Li Peking University xk-lrc@pku.edu.cn Zhenguo Li Huawei Noah s Ark Lab Li.Zhenguo@huawei.com Liwei Wang Key Laboratory of Machine Perception, MOE, School of EECS, Institute for Artificial Intelligence, Peking University wanglw@cis.pku.edu.cn |
| Pseudocode | Yes | Algorithm 1: Model Selection |
| Open Source Code | No | Our experiments is conducted in Domain Bed: https://github.com/facebookresearch/Domain Bed. This statement indicates the use of an existing framework for conducting experiments, not an explicit release of the authors' own implementation code for their proposed methodology. |
| Open Datasets | Yes | We train our model on three benchmark OOD datasets (PACS [34], Office Home [59], VLCS [57]) |
| Dataset Splits | Yes | Algorithm 1: Model Selection Input: available dataset Xavail = (Xtrain, Xval)... Accf compute validation accuracy of f using Xval |
| Hardware Specification | No | The paper does not specify any particular hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Domain Bed' and 'GPU kernel density estimation' but does not provide specific version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | We train 200 models using different algorithms, penalties, learning rates, and epoch. For more details about the experiments, see Appendix 4. |