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..
SAGA with Arbitrary Sampling
Authors: Xun Qian, Zheng Qu, Peter Richtárik
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested SAGA-AS to solve the logistic regression problem (23) on 3 different datasets: w8a, a9a and ijcnn14. The experiments presented in Section 5.1 and 5.2 are tested for λ1 = 0 and λ2 = 1e 5, which is of the same order as the number of samples in the three datasets. In Section 5.3 we test on the unregularized problem with λ1 = λ2 = 0. In all the plots, the x-axis records the number of pass of the dataset. More experiments can be found in the Suppl. |
| Researcher Affiliation | Academia | 1King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia 2University of Hong Kong, Hong Kong 3Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia. |
| Pseudocode | Yes | Algorithm 1 SAGA with Arbitrary Sampling (SAGA-AS) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the SAGA-AS method, nor does it include a link to a code repository. |
| Open Datasets | Yes | We tested SAGA-AS to solve the logistic regression problem (23) on 3 different datasets: w8a, a9a and ijcnn14. ... 4https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions using specific datasets but does not explicitly describe the training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments, such as CPU or GPU models, or cloud computing instances. |
| Software Dependencies | No | The paper mentions “libsvmtools/datasets/” as a source for datasets but does not provide specific version numbers for any software dependencies, libraries, or programming languages used for the implementation or experiments. |
| Experiment Setup | Yes | The experiments presented in Section 5.1 and 5.2 are tested for λ1 = 0 and λ2 = 1e 5, which is of the same order as the number of samples in the three datasets. In Section 5.3 we test on the unregularized problem with λ1 = λ2 = 0. ... We compare uniform sampling SAGA (SAGA-UNI) with importance sampling SAGA (SAGA-IP), as described in Section 3.3 , on three values of τ {1, 10, 50}. |