Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis
Authors: Youcheng Niu, Jinming Xu, Ying Sun, Yan Huang, Li Chai
JMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present two numerical experiments to test the performance of the proposed Lo PA algorithm and verify the theoretical findings. Specifically, the first experiment considers classification problems for a 10-class classification task, while the second focuses on hyperparameter optimization in l2-regularized binary logistic regression problems for a two-class classification task. [...] The experiment results for loss, training accuracy, and testing accuracy are presented in Figures 1 and 2. |
| Researcher Affiliation | Academia | College of Control Science and Engineering, Zhejiang University, China School of Electrical Engineering and Computer Science, The Pennsylvania State University, USA |
| Pseudocode | Yes | Algorithm 1 Lo PA |
| Open Source Code | No | The paper does not provide a concrete link to source code, an explicit statement of code release, or mention of code in supplementary materials for the methodology described. |
| Open Datasets | Yes | We employ MNIST datasets to train m personalized classifiers for a 10-class classification task. [...] We conduct the experiment across various datasets including MNIST (784 features, 12000 samples for digits 0 and 1 ), covtype (54 features, 90000 samples for the Lodgepole and Ponderosa pine classes), and cifar10 (3072 features, 6000 samples for the dog and horse classes). |
| Dataset Splits | No | The paper describes how samples are distributed among nodes (e.g., 'each node having 14000 samples', 'validation and training sets for each node are randomly assigned with a uniform number of samples') but does not provide specific train/test/validation split percentages or absolute counts for the overall dataset or for individual nodes, which are necessary for full reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., library names like PyTorch 1.9, or solver versions). |
| Experiment Setup | Yes | The step-sizes are set as α = 0.01, β = 0.01, λ = 0.008, γ = 0.4, τ = 0.4 both for Lo PA-LG and Lo PA-GT. [...] mini-batch sizes are set to 50 for all algorithms. [...] The first layer of the classifier contains 28 neurons, while the second layer contains 10 neurons. |