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].
A unified variance-reduced accelerated gradient method for convex optimization
Authors: Guanghui Lan, Zhize Li, Yi Zhou
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide extensive experimental results to demonstrate the advantages of Varag over several state-of-the-art methods for solving some well-known ML models, e.g., logistic regression, Lasso, etc. We defer the proofs of the main results in Appendix A. |
| Researcher Affiliation | Collaboration | Guanghui Lan H. Milton Stewart School of Industrial & Systems Engineering Georgia Institute of Technology Atlanta, GA 30332 EMAIL Zhize Li Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084, China EMAIL Yi Zhou IBM Almaden Research Center San Jose, CA 95120 EMAIL |
| Pseudocode | Yes | Algorithm 1 The variance-reduced accelerated gradient (Varag ) method. Algorithm 2 Stochastic accelerated variance-reduced stochastic gradient descent (Stochastic Varag ) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | For all experiments, we use public real datasets downloaded from UCI Machine Learning Repository [10] and uniform sampling strategy to select fi. Diabetes (m = 1151), Breast Cancer Wisconsin (m = 683), Parkinsons Telemonitoring (m = 5875) |
| Dataset Splits | No | The paper does not provide specific details on dataset split percentages, counts, or explicit methodology for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | Yes | The algorithmic parameters for SVRG++ and Katyushans are set according to [2] and [1], respectively, and those for Varag are set as in Theorem 1. The algorithmic parameters for SVRG++ and Katyushans are set according to [2] and [1], respectively, and those for Varag are set as in Theorem 2. The algorithmic parameters for FGM and Varag are set according to [21] and Theorem 3, respectively. |