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..
Conditional gradient methods for stochastically constrained convex minimization
Authors: Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary numerical experiments are provided for illustrating the prac tical performance of the methods. and section 5. Numerical Experiments |
| Researcher Affiliation | Academia | 1 Ecole Polytechnique ed de Lausanne, Switzer F erale land. Correspondence to: Maria-Luiza Vladarean <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 H-1SFW and Algorithm 2 H-SPIDER-FW |
| Open Source Code | No | No explicit statement or link for open-source code release for the described methodology was found. |
| Open Datasets | Yes | In order to compare against existing work, we adopt the MNIST dataset (k = 10) (Le Cun & Cortes, 2010) and We run our algorithms on three graphs of different sizes from the Network Repository dataset (Rossi & Ahmed, 2015) |
| Dataset Splits | No | The paper uses well-known datasets but does not explicitly state the specific train/validation/test splits used for its experiments. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | For a fair comparison, we sweep the parameter β0 for the three algorithms in the range [1e-7, 1e1]. We settle for 1e-7, 1e-7 and 1e-5 for SHCGM, H-1SFW and H SPIDER-FW, respectively. For H-1SFW and SHCGM, we choose the batch size to be 1% of the data. and The batchsize for H-1SFW and SHCGM is set to 5%. |