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..
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
Authors: Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gurbuzbalaban, Stefanie Jegelka, Hongzhou Lin
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental results that demonstrate the effectiveness of the proposed algorithm on highly ill-conditioned problems. |
| Researcher Affiliation | Academia | Yossi Arjevani NYU EMAIL Bruna NYU EMAIL Can Rutgers University EMAIL Gürbüzbalaban Rutgers University EMAIL Jegelka MIT EMAIL Lin MIT EMAIL |
| Pseudocode | Yes | Algorithm 1 Decentralized Augmented Lagrangian framework; Algorithm 2 Accelerated Decentralized Augmented Lagrangian framework; Algorithm 3 IDEAL: Inexact Acc-Decentralized Augmented Lagrangian framework |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the proposed methodology is publicly available. |
| Open Datasets | Yes | To facilitate a simple comparison between existing state-of-the-art algorithms, we consider an ℓ2-regularized logistic regression task over two classes of the MNIST [24]/CIFAR-10 [23] benchmark datasets. ... [24] Y. Le Cun, C. Cortes, and C. Burges. Mnist handwritten digit database. ATT Labs [Online], 2, 2010. URL http://yann.lecun.com/exdb/mnist. |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR-10 datasets but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, counts, or explicit references to standard splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models, processor types, or memory. |
| Software Dependencies | No | The paper mentions using logistic regression and convolutional kernel networks but does not provide specific software dependencies (e.g., programming languages, libraries, or frameworks) with version numbers. |
| Experiment Setup | Yes | We set the inner iteration counter to be Tk = 100 for all algorithms, and use the theoretical stepsize schedule. The decentralized environment is modelled in a synthetic setting, where the communication time is steady and no latency is encountered. To demonstrate the effect of the underlying network architecture, we consider: a) a circular graph, where the agents form a cycle; b) a Barbell graph, where the agents are split into two complete subgraphs, connected by a single bridge (shown in Figure 2 in the appendix). |