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].
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Authors: Zheng Qu, Peter Richtarik, Martin Takac, Olivier Fercoq
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7. Numerical Experiments In our first experiment (Figure 1) we compare SDNA and our new minibatch version of SDCA on two real (mushrooms: d = 112, n = 8, 124; cov: d = 54, n = 522, 911) and one synthetic (d = 1, 024, n = 2, 048) dataset. |
| Researcher Affiliation | Academia | Zheng Qu EMAIL Department of Mathematics, The University of Hong Kong, Hong Kong Peter Richt arik EMAIL School of Mathematics, The University of Edinburgh, UK Martin Tak aˇc EMAIL Industrial and Systems Engineering, Lehigh University, USA Olivier Fercoq EMAIL LTCI, CNRS, T el ecom Paris-Tech, Universit e Paris-Saclay, France |
| Pseudocode | Yes | Algorithm 1 Proximal Overlapping-Block CD |
| Open Source Code | No | The paper does not contain any statements about making the source code for their proposed method publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | In our first experiment (Figure 1) we compare SDNA and our new minibatch version of SDCA on two real (mushrooms: d = 112, n = 8, 124; cov: d = 54, n = 522, 911) and one synthetic (d = 1, 024, n = 2, 048) dataset. |
| Dataset Splits | No | The paper states the use of 'mushrooms', 'cov', and a 'synthetic' dataset, but it does not specify any training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions that 'all experiments are done using a single core' but does not provide any specific details about the hardware used, such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide any specific software dependency details, such as library names with version numbers (e.g., Python 3.x, TensorFlow x.x, PyTorch x.x). |
| Experiment Setup | Yes | In both cases, we used λ = 1/n as the regularization parameter and g(w) = 1/2 ||w||^2_2. Comparison of SDNA and SDCA for minibatch sizes τ = 1, 32, 256. Runtime of SDNA for minibatch sizes τ = 1, 4, 16, 32, 64. |