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..
Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization
Authors: Ilya Kuruzov, Gesualdo Scutari, Alexander Gasnikov
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary numerical experiments support our theoretical findings, demonstrating superior performance in convergence speed and scalability. |
| Researcher Affiliation | Academia | Ilya Kuruzov Innopolis University EMAIL. Gesualdo Scutari Purdue University EMAIL. Alexander Gasnikov Innopolis University EMAIL |
| Pseudocode | Yes | Algorithm 1 Data: ... Algorithm 2 Backtracking(...) |
| Open Source Code | Yes | code in the form of an attached archive. |
| Open Datasets | No | Ridge regression: It is an instance of (P), with fi(x) = Aixi bi 2 + Ď xi 2 2, where we set Ai R20 300, bi R20, and Ď = 0.1.The elements of Ai, bi are independently sampled from the standard normal distribution |
| Dataset Splits | No | The paper uses synthetic data generated by sampling elements from a standard normal distribution but does not specify any training, validation, or test splits. |
| Hardware Specification | Yes | All experiments are run on Acer Swift 5 SF514-55TA56B6, with processor Intel(R) Core(TM) i5-8250U @ CPU 1.60GHz, 1800 MHz. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For EXTRA and NIDS we use a grid-search tuning, chosen to achieve the best practical performance. Algorithm 1 and Algorithm 3 are simulated under the following choice of the line-search parameters satisfying Corollary 4.1: γk = (k + 2)/(k + 1), δ = 1. For all the algorithms we used the Metropolis-Hastings weight matrix W GW [34]. |