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..
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. |