Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Authors: Fan Bao, Guoqiang Wu, Chongxuan LI, Jun Zhu, Bo Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on feature learning and data reweighting for noisy labels, we corroborate our theoretical findings. |
| Researcher Affiliation | Academia | Fan Bao , Guoqiang Wu , Chongxuan Li , Jun Zhu , Bo Zhang Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua-Huawei Joint Center for AI BNRist Center, State Key Lab for Intell. Tech. & Sys., Tsinghua University, Beijing, China bf19@mails.tsinghua.edu.cn,{guoqiangwu90, chongxuanli1991}@gmail.com, {dcszj, dcszb}@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 Unrolled differentiation for hyperparameter optimization; Algorithm 2 Cross-validation for hyperparameter optimization |
| Open Source Code | Yes | See https://github.com/baofff/stability_ho. |
| Open Datasets | Yes | In feature learning, we evaluate all algorithms on the Omniglot dataset [22] following [10]. [...] In data reweighting, we evaluate all algorithms on the MNIST dataset [23] following [35]. |
| Dataset Splits | Yes | We randomly select 100 classes and obtain a training, validation and testing set of size 500, 100, and 1000 respectively. [...] We randomly select 2000, 200, and 1000 images for training, validation and testing respectively. |
| Hardware Specification | Yes | Each experiment takes at most 10 hours in one Ge Force GTX 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions using SGD but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We employ a mini-batch version of SGD in both levels of UD with a learning rate 0.1 and batch size 50. [...] We employ a mini-batch version of SGD in both levels of UD with a batch size 100. The learning rate is 10 in the outer level and 0.3 in the inner level. |