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..
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
Authors: Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary experiments on logistic regression problem indicate that our method is competitive with Catalyst-SVRG in practice.2 |
| Researcher Affiliation | Academia | 1Tel Aviv University 2Stanford University. |
| Pseudocode | Yes | Algorithm 1: RECAPP |
| Open Source Code | Yes | Code available at: github.com/yaircarmon/recapp. |
| Open Datasets | Yes | We consider logistic regression on three datasets from lib SVM (lib): covertype (n = 581, 012, d = 54), real-sim (n = 72, 309, d = 20, 958), and a9a (n = 32, 561, d = 123). For each dataset we rescale the feature vectors to using unit Euclidean norm so that each fi is exactly 0.25-smooth. ... The LIBSVM data webpage. URL https://www. csie.ntu.edu.tw/ cjlin/libsvmtools/ datasets/. |
| Dataset Splits | No | The paper describes dataset usage but does not provide specific training, validation, or test splits. It only states that feature vectors were rescaled and no ℓ2 regularization was added. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models, memory, or cloud instances) used for the experiments. |
| Software Dependencies | No | The paper mentions "Python" and the "Numba" package, but it does not specify any version numbers for these software components. |
| Experiment Setup | Yes | For each dataset we rescale the feature vectors to using unit Euclidean norm so that each fi is exactly 0.25-smooth. We do not add ℓ2 regularization to the logistic regression objective. ... For RECAPP and Catalyst, we tune the proximal regularization parameter λ (called κ in (Lin et al., 2017)). For each problem and each algorithm, we test λ values of the form αL/n, where L = 0.25 is the objective smoothness, n is the dataset size and α in the set {0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1.0, 3.0, 10.0}. ... In Algorithm 2 we set the parameters j0 = 0 and we test p {0, 0.1, 0.25, 0.5}. |