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..
Projection onto Minkowski Sums with Application to Constrained Learning
Authors: Joong-Ho Won, Jason Xu, Kenneth Lange
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirical advantages in runtime and accuracy over competitors in applications to ℓ1,p-regularized learning, constrained lasso, and overlapping group lasso.Results of the simulation study are summarized in Figure 1. |
| Researcher Affiliation | Academia | 1Department of Statistics, Seoul National University 2Department of Statistical Science, Duke University 3University of California, Los Angeles. |
| Pseudocode | Yes | Algorithm 1 Projection onto a Minkowski sum of sets |
| Open Source Code | No | The paper states 'We compare a MATLAB implementation of our algorithm' but does not provide specific access information or a link to the source code for their own method. |
| Open Datasets | No | The paper describes experiments using 'randomly generated inputs x' for the ℓ1,p-overlapping group lasso and 'randomly sampled A and noisy response b' for the constrained lasso, without referencing any publicly available datasets, links, or formal citations. |
| Dataset Splits | No | The paper describes using randomly generated inputs and sampled matrices/responses for its experiments, which does not involve traditional fixed training, validation, and test dataset splits. |
| Hardware Specification | Yes | The simulation was run on a Linux machine with two Intel Xeon E5-2650v4 (2.20GHz) CPUs.The simulation was run on a Linux machine with two Intel Xeon E5-2680v2 (2.80GHz) CPUs with 256GB memory. |
| Software Dependencies | No | The paper mentions using 'MATLAB,' 'SLEP (Liu et al., 2011),' and 'Gurobi (Gurobi Optimization, LLC, 2018)' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For each combination of d = 103, 104, 105, 106 and g = 10, 20, 50, 100, proximal maps were computed using both methods for 50 randomly generated inputs x; λ = 2.1 was used.Four sparsity levels were tried: λ/λmax = 0.2, 0.4, 0.6, 0.8, where λmax is the maximal sparsity level found by solving a linear program via Gurobi (Gaines et al., 2018, Sect. 3). |