Using Benson’s Algorithm for Regularization Parameter Tracking
Authors: Joachim Giesen, Sӧren Laue, Andreas Lӧhne, Christopher Schneider3689-3696
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments for the Elastic Net on real world data sets demonstrate the effectiveness of Benson s algorithm for regularization parameter tracking. |
| Researcher Affiliation | Academia | Joachim Giesen, S oren Laue, Andreas L ohne Friedrich-Schiller-Universit at Jena Faculty of Mathematics and Computer Science Ernst-Abbe-Platz 2 07743 Jena, Germany Christopher Schneider Ernst-Abbe-Hochschule Jena Fachbereich Grundlagenwissenschaften Carl-Zeiss-Promenade 2 07745 Jena, Germany |
| Pseudocode | Yes | Algorithm 1 Benson Algorithm |
| Open Source Code | No | The paper mentions using |
| Open Datasets | Yes | For our experiments, we use the following data sets, which are well-known from the literature: ALLAML with m = 7,129 features and n = 72 instances, arcene with m = 10,000 and n = 200, GLI-85 with m = 22,283 and n = 85, GLIOMA with m = 4,434 and n = 50, Prostate-GE with m = 5,966 and n = 102, SMK-CAN-187 with m = 19,993 and n = 187, Carcinom with m = 9,182 and n = 174, 14cancer with m = 16,063 and n = 198. |
| Dataset Splits | No | For all data sets, 70% of the data have been used for training, and 30% have been hold out for testing. There is no explicit mention of a validation split percentage or how it was used. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | Yes | In our implementation of Algorithm 1, we used Gurobi (Gurobi Optimization 2016) for solving the scalarized problems (Pw). Facet enumeration is done by bensolve tools (Ciripoi, L ohne, and Weißing 2018). |
| Experiment Setup | Yes | Second, we compute a fine-mesh solution with Grid Search by solving (EN ) for all α, β {0, 0.01, 0.02, . . . , 1}. We then run Benson s algorithm with approximation errors ε = 0.1 (for the first six data sets) and ε = 1 (for the last two data sets), resp., depending on the scale of the objective function values. We fixed the direction parameter c = (1, . . . , 1)T. |