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..
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains
Authors: Katharina Blechschmidt, Joachim Giesen, Soeren Laue
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results for kernelized support vector machines and the elastic net confirm the theoretical complexity analysis. |
| Researcher Affiliation | Academia | Friedrich-Schiller-Universit at Jena, Germany |
| Pseudocode | No | Section 4 describes the algorithm's steps in prose ('Initialization.' and 'Iteration.') rather than in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper states 'In our implementation of the adaptive algorithm from Section 4 we used the LIBSVM package, see (Fan et al., 2005), to compute a near optimal dual solution at a given grid vertex...' and 'In our implementation of the adaptive algorithm from Section 4 we used GLMNET, see (Friedman et al., 2010), for solving the primal optimization problem...' but does not provide a link to the authors' own source code for the described methodology. |
| Open Datasets | Yes | The data sets that have been used in our experiments were obtained from the LIBSVM website, see (Lin) for a description. (Lin) LIBSVM Tools. Data sets available at www.csie.ntu.edu.tw/~cjlin/ libsvmtools/datasets/. |
| Dataset Splits | Yes | In Figure 2(middle) a 10-fold cross-validation plot is shown for the same data set. In Figure 3(middle/left) a 10-fold cross-validation RMSE plot is shown for the same data set. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'LIBSVM package' and 'GLMNET' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We considered the two-dimensional parameter space (c, γ) with c [2^10, 2^10] and γ [2^10, 2^10], and a uniform grid with vertices at (2^i, 2^j), where i and j were incremented in steps of 0.05, i.e., the grid had 400 x 400 = 160,000 vertices. We considered parameter values λ [0, 1] and c [2^10, 2^5]. |