Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks
Authors: Shuoguang Yang, Xuezhou Zhang, Mengdi Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithm on the examples of hyperparameter tuning and decentralized reinforcement learning. Simulated experiments confirmed that our algorithm achieves the state-of-the-art training efficiency and test accuracy. |
| Researcher Affiliation | Academia | Shuoguang Yang IEDA, HKUST yangsg@ust.hk Xuezhou Zhang Princeton University xz7392@princeton.edu Mengdi Wang Princeton University mengdiw@princeton.edu |
| Pseudocode | Yes | Algorithm 1 Gossip-Based Decentralized Stochastic Bilevel Optimization |
| Open Source Code | No | We will provide the code via github in the camera-ready version stage. |
| Open Datasets | Yes | Hyper-parameter Optimization We consider federated hyper-parameter optimization (2) for a handwriting recognition problem over the Australia handwriting dataset (Chang and Lin, 2011) |
| Dataset Splits | No | Before testing Alg. 1, we first randomly split the dataset for training and validation, and then allocates both training and validation dataset over K agents. |
| Hardware Specification | No | We run the experiments on a single server desktop computer. |
| Software Dependencies | No | The paper does not specify any software names with version numbers. |
| Experiment Setup | Yes | We then run Algorithm 1 for T = 20000 iterations, with b = 200, t = 0.1 K/T, and βt = γt = 10 |