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..
A Universal Catalyst for First-Order Optimization
Authors: Hongzhou Lin, Julien Mairal, Zaid Harchaoui
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the Catalyst acceleration on three methods that have never been accelerated in the past: SAG [24], SAGA [6], and MISO-Prox. We focus on ℓ2-regularized logistic regression, where the regularization parameter µ yields a lower bound on the strong convexity parameter of the problem. We use three datasets used in [14], namely real-sim, rcv1, and ocr, which are relatively large, with up to n = 2 500 000 points for ocr and p = 47 152 variables for rcv1. |
| Researcher Affiliation | Academia | Hongzhou Lin1, Julien Mairal1 and Zaid Harchaoui1,2 1Inria 2NYU EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Catalyst input initial estimate x0 Rp, parameters κ and α0, sequence (εk)k 0, optimization method M; |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the methodology or provide links to a code repository. |
| Open Datasets | Yes | We use three datasets used in [14], namely real-sim, rcv1, and ocr, which are relatively large, with up to n = 2 500 000 points for ocr and p = 47 152 variables for rcv1. |
| Dataset Splits | No | The paper states it uses datasets 'real-sim, rcv1, and ocr' but does not specify any training, validation, or test splits (e.g., '80/10/10 split' or specific sample counts for each). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as exact GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions 'Python' in Appendix E regarding implementation details but does not provide specific version numbers for Python or any other key software libraries or solvers used in the experiments. |
| Experiment Setup | Yes | We compare MISO, SAG, and SAGA with their default parameters, which are recommended by their theoretical analysis (step-sizes 1/L for SAG and 1/3L for SAGA), and study several accelerated variants. The values of κ and ρ and the sequences (εk)k 0 are those suggested in the previous sections, with η=0.1 in (10). Other implementation details are presented in Appendix E. |