Conditional gradient methods for stochastically constrained convex minimization
Authors: Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 <maria-luiza.vladarean@epfl.ch>. |
| 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%. |