Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity
Authors: Bingqing Song, Ioannis Tsaknakis, Chung-Yiu Yau, Hoi-To Wai, Mingyi Hong
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary Numerical Experiments. We conclude by presenting a numerical experiment for the UCI Tom’s Hardware dataset using Alg. 2 where we applied blog(t+100)c rounds of communication at the t-th iteration for the CHOCO-GOSSIP subroutine; see Appendix F.1. We consider a ring network with K = 5 agents, each one has 500 or 1000 samples (thus making N = 2500, or N = 5000). We construct D-dimensional features from the dataset as NTK features [Bietti and Mairal, 2019]. In Fig. 1, we train a least square regression model in the overparameterized regime. |
| Researcher Affiliation | Academia | Bingqing Song Department of ECE University of Minnesota email:EMAIL Ioannis Tsaknakis Department of ECE University of Minnesota email:EMAIL Chung-Yiu Yau Department of SEEM Chinese University of Hong Kong email:EMAIL Hoi-To Wai Department of SEEM Chinese University of Hong Kong email:EMAIL Mingyi Hong Department of ECE University of Minnesota email:EMAIL |
| Pseudocode | Yes | Algorithm 1 Limited Communication Distributed Optimization Algorithm ... Algorithm 2 Decentralized Gradient Descent with Compressed Comm. via Linear Compression |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code open, nor does it include a link to a code repository for the described methodology. |
| Open Datasets | Yes | Preliminary Numerical Experiments. We conclude by presenting a numerical experiment for the UCI Tom’s Hardware dataset using Alg. 2 where we applied blog(t+100)c rounds of communication at the t-th iteration for the CHOCO-GOSSIP subroutine; see Appendix F.1. We consider a ring network with K = 5 agents, each one has 500 or 1000 samples (thus making N = 2500, or N = 5000). We construct D-dimensional features from the dataset as NTK features [Bietti and Mairal, 2019]. In Fig. 1, we train a least square regression model in the overparameterized regime. Available: https://archive.ics.uci.edu/ml/datasets/Buzz+in+social+media+ |
| Dataset Splits | No | The paper mentions training a model on the dataset but does not specify details regarding train/validation/test splits or cross-validation setup for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | We consider a ring network with K = 5 agents, each one has 500 or 1000 samples (thus making N = 2500, or N = 5000). ... we applied blog(t+100)c rounds of communication at the t-th iteration for the CHOCO-GOSSIP subroutine. |