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 [1].
Stochastic Dual Coordinate Ascent with Adaptive Probabilities
Authors: Dominik Csiba, Zheng Qu, Peter Richtarik
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also propose Ada SDCA+: a practical variant which in our experiments outperforms existing non-adaptive methods. |
| Researcher Affiliation | Academia | Dominik Csiba EMAIL University of Edinburgh Zheng Qu EMAIL University of Edinburgh Peter Richt arik EMAIL University of Edinburgh |
| Pseudocode | Yes | Algorithm 1 Ada SDCA |
| Open Source Code | No | The paper does not provide explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We used 5 different datasets: w8a, dorothea, mushrooms, cov1 and ijcnn1 (see Table 2). |
| Dataset Splits | No | The paper mentions using datasets (w8a, dorothea, mushrooms, cov1, ijcnn1) but does not provide specific details on how these datasets were split into training, validation, and test sets, or reference standard splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instance types) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or tools used in the experiments. |
| Experiment Setup | Yes | In all our experiments we used γ = 1 and λ = 1/n. |