Projection onto Minkowski Sums with Application to Constrained Learning
Authors: Joong-Ho Won, Jason Xu, Kenneth Lange
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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). |