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..
Continuous-time Analysis of Anchor Acceleration
Authors: Jaewook Suh, Jisun Park, Ernest Ryu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present an adaptive method inspired by the continuous-time analyses and establish its effectiveness through theoretical analyses and experiments. We now show an experiment with the method of Theorem 7.2 applied to a decentralized compressed sensing problem Shi et al. [63]. We show the results in Figure 2. |
| Researcher Affiliation | Academia | Jaewook J. Suh Seoul National University EMAIL Jisun Park Seoul National University EMAIL Ernest K. Ryu Seoul National University EMAIL |
| Pseudocode | No | The paper describes algorithms and methods using mathematical notation and text, but it does not contain a clearly labeled pseudocode block or algorithm section. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the methodology or provide a link to a source-code repository. |
| Open Datasets | Yes | We solve a compressed sensing problem of Shi et al. [63]. |
| Dataset Splits | No | The paper uses a compressed sensing problem but does not specify dataset splits (e.g., percentages, sample counts, or explicit predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions applying PG-EXTRA but does not list specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | We choose the dimension of signal d = 100, the number of agents n = 20, the number of measurement for each agent mi = 4, ℓ1-regularization parameter ρ = 0.01, and algorithm parameter α = 0.01. |