Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach
Authors: Prashant Khanduri, Haibo Yang, Mingyi Hong, Jia Liu, Hoi To Wai, Sijia Liu
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of all the algorithms on real world datasets from the UCI repository. Specifically, we present the results on National Advisory Committee for Aeronautics (NACA) airfoil noise dataset (Lau & L opez, 2009)... |
| Researcher Affiliation | Collaboration | University of Minnesota, The Ohio State University, CUHK, Michigan State University, MIT-IBM Watson AI Lab, IBM Research |
| Pseudocode | Yes | Algorithm 1 Approximation: Local Kernel Estimation |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating the release of its source code. |
| Open Datasets | Yes | We evaluate the performance of all the algorithms on real world datasets from the UCI repository. Specifically, we present the results on National Advisory Committee for Aeronautics (NACA) airfoil noise dataset (Lau & L opez, 2009) |
| Dataset Splits | Yes | Each node utilizes 70% of its data for training and 30% for testing purposes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Additional experiments on different datasets and classification problems, as well as the detailed parameter settings, are included in the Appendix A. |