How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?
Authors: Jun Chen, Hong Chen, Bin Gu
NeurIPS 2024 | Venue 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 EMAIL Hong Chen College of Informatics, Huazhong Agricultural University, China Engineering Research Center of Intelligent Technology for Agriculture, China EMAIL Bin Gu School of Artificial Intelligence, Jilin University, China Mohamed bin Zayed University of Artificial Intelligence EMAIL |
| 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. |