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..
Delay-agnostic Asynchronous Coordinate Update Algorithm
Authors: Xuyang Wu, Changxin Liu, Sindri Magnússon, Mikael Johansson
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of DEGAS is demonstrated by numerical experiments on classification problems. We evaluate the practical performance of DEGAS on Lasso and regularized logistic regression problems on the CIFAR-100 dataset (Krizhevsky et al., 2009). We plot the convergence in terms of the number of computed Ti in Figures 2 3 |
| Researcher Affiliation | Academia | 1Division of Decision and Control Systems, EECS, KTH Royal Institute of Technology, Stockholm, Sweden 2Department of Computer and System Science, Stockholm University, Stockholm, Sweden. |
| Pseudocode | Yes | Algorithm 1 DEGAS 1: Setup: initial iterate x(0). 2: Initialization: the master sets x = x(0) and broadcasts x to all workers. 3: while not interrupted by master: each worker w [n] asynchronously and continuously do 4: receive x from the master and assign xw = x. 5: sample i [m] uniformly at random. 6: compute Ti(xw). 7: send (Ti(xw), i) to the master. 8: end while 9: while not converged: the master do 10: receive (Ti(xw), i) from a worker w. 11: update xi Ti(xw). 12: send x to the worker w. 13: end while |
| Open Source Code | No | The paper does not provide a direct link to a source code repository or an explicit statement of code release for the methodology described. |
| Open Datasets | Yes | We evaluate the practical performance of DEGAS on Lasso and regularized logistic regression problems on the CIFAR-100 dataset (Krizhevsky et al., 2009). |
| Dataset Splits | No | The paper mentions using the CIFAR-100 dataset but does not explicitly provide details about specific train/validation/test splits, such as percentages or sample counts. |
| Hardware Specification | Yes | We set m = 20 and implement all the methods on a 10-core machine (1 master and 9 workers) |
| Software Dependencies | No | The paper mentions 'MPI4py (Dalcın et al., 2008)' but does not provide specific version numbers for MPI4py or any other software dependencies, which are required for reproducibility. |
| Experiment Setup | Yes | In these methods, we choose the operator T as (20) with γ = 1/L in BCD and (29) in the extended ADMM. We set m = 20... We consider both theoretical and hand-tuned parameters. In the former setting, we fine-tune the step-size of ARock within its theoretical range... while the other two methods have no parameters to tune. In the hand-tune step-size setting, we run all the methods for finding the fixed point of Id +λ(T Id), λ > 0 and tune λ. |