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..
Primal-Dual Rates and Certificates
Authors: Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we illustrate the usefulness of our framework by showcasing it for two important applications, each one showing two algorithm examples for optimizing (A). The top row of Figure 2 shows the primal-dual convergence of Algorithm 1 (CD) as well as the accelerated variant of CD (APPROX, Fercoq & Richt arik (2015)), both applied to the Lasso problem (A). On the bottom row of Figure 2 we compare CD with its accelerated variant on two benchmark datasets.5 We have chosen λ = 1/n. |
| Researcher Affiliation | Collaboration | Celestine D unner EMAIL IBM Research, Z urich, Switzerland Simone Forte EMAIL ETH Z urich, Switzerland Martin Tak aˇc EMAIL Lehigh University, USA Martin Jaggi EMAIL ETH Z urich, Switzerland |
| Pseudocode | Yes | Algorithm 1 Coordinate Descent on D(α) |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | On the bottom row of Figure 2 we compare CD with its accelerated variant on two benchmark datasets.5 We have chosen λ = 1/n. Footnote 5: Available from csie.ntu.edu.tw/ cjlin/libsvmtools/datasets. |
| Dataset Splits | No | The paper mentions using benchmark datasets but does not specify exact training/validation/test split percentages, sample counts, or explicitly reference predefined splits with citations. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | On the bottom row of Figure 2 we compare CD with its accelerated variant on two benchmark datasets.5 We have chosen λ = 1/n. |