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].
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing
Authors: Tom Goldstein, Min Li, Xiaoming Yuan
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that adaptive PDHG has strong advantages over non-adaptive methods in terms of both ef๏ฌciency and simplicity for the user. |
| Researcher Affiliation | Academia | Thomas Goldstein Department of Computer Science University of Maryland Min Li School of Economics and Management Southeast University Xiaoming Yuan Department of Mathematics Hong Kong Baptist University |
| Pseudocode | Yes | Algorithm 1 Adaptive PDHG |
| Open Source Code | No | The paper does not provide explicit access to source code or links to a repository for the methodology described. |
| Open Datasets | Yes | We test our methods on (8) using the synthetic problem suggested in [21]. [21] Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58:267 288, 1994. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We terminate the algorithms when both the primal and dual residual norms (i.e. kpkk and kdkk) are smaller than 0.05. In our implementation we use 0 = = .95. We use c = 0.9 in our experiments. |