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].
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}. |