Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
Authors: Jie Hu, Vishwaraj Doshi, Do Young Eun
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we simulate our SA-SRRW algorithm on the wiki Vote graph (Leskovec & Krevl, 2014), comprising 889 nodes and 2914 edges. ... Our results are presented in Figures 2 and 3, where each experiment is repeated 100 times. |
| Researcher Affiliation | Collaboration | Jie Hu 1, Vishwaraj Doshi 2, Do Young Eun1 1North Carolina State University, 2IQVIA Inc. {jhu29,dyeun}@ncsu.edu, vishwaraj.doshi@iqvia.com |
| Pseudocode | No | The paper describes algorithm steps using mathematical equations and textual descriptions (e.g., 'Draw: Xn+1 KXn, [xn] (4a) Update: xn+1 = xn + γn+1(δXn+1 xn), (4b) θn+1 = θn + βn+1H(θn, Xn+1), (4c)') but does not present them in a clearly labeled 'Pseudocode' or 'Algorithm' block format. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In this section, we simulate our SA-SRRW algorithm on the wiki Vote graph (Leskovec & Krevl, 2014), comprising 889 nodes and 2914 edges. ... we consider the following L2 regularized binary classification problem: ... from the ijcnn1 dataset (with 22 features, i.e., si R22) from LIBSVM (Chang & Lin, 2011), ... Figure 2c on the smaller Dolphins graph (Rossi & Ahmed, 2015) 62 nodes and 159 edges |
| Dataset Splits | No | The paper mentions using specific datasets but does not explicitly provide details about training, validation, and test splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments, such as particular CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | We configure the SRRW s base Markov chain P as the MHRW with a uniform target distribution µ = 1 N 1. For distributed optimization, we consider the following L2 regularized binary classification problem: ... and penalty parameter κ = 1. ... We fix the step size βn = (n + 1) 0.9 for the SA iterates and adjust γn = (n + 1) a in the SRRW iterates to cover all three cases discussed in this paper: (i) a = 0.8; (ii) a = 0.9; (iii) a = 1. We use mean square error (MSE), i.e., E[ θn θ 2], to measure the error on the SA iterates. Our results are presented in Figures 2 and 3, where each experiment is repeated 100 times. |