How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?
Authors: Jun Chen, Hong Chen, Bin Gu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper aims to reveal the impacts by developing a theoretical analysis for two derivative-free algorithms, black-box SCGD and SCSC. |
| Researcher Affiliation | Academia | Jun Chen College of Informatics, Huazhong Agricultural University, China cj850487243@163.com Hong Chen College of Informatics, Huazhong Agricultural University, China Engineering Research Center of Intelligent Technology for Agriculture, China chenh@mail.hzau.edu.cn Bin Gu School of Artificial Intelligence, Jilin University, China Mohamed bin Zayed University of Artificial Intelligence jsgubin@gmail.com |
| Pseudocode | Yes | Algorithm 1 (Black-box) SCGD / SCSC; Algorithm 2 FOO-based VFL / VFL-CZOFO |
| Open Source Code | No | The paper's contributions are from the theoretical analysis perspective. There isn't any data or code. |
| Open Datasets | No | The paper's contributions are from the theoretical analysis perspective, and it does not use or provide access to any specific dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation dataset splits. |
| Hardware Specification | No | The paper's contributions are from the theoretical analysis perspective, and it does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper's contributions are from the theoretical analysis perspective, and it does not describe any specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper's contributions are from the theoretical analysis perspective, and it does not provide specific experimental setup details like hyperparameters or training configurations. |