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 [1].
Optimal Column Subset Selection by A-Star Search
Authors: Hiromasa Arai, Crystal Maung, Haim Schweitzer
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on common datasets show that the proposed algorithm can effectively select columns from moderate size matrices, typically improving by orders of magnitude the run time of exhaustive search. |
| Researcher Affiliation | Academia | Hiromasa Arai The University of Texas at Dallas 800 W Campbell Road Richardson, Texas 75080 EMAIL Crystal Maung The University of Texas at Dallas 800 W Campbell Road Richardson, Texas 75080 EMAIL Haim Schweitzer The University of Texas at Dallas 800 W Campbell Road Richardson, Texas 75080 EMAIL |
| Pseudocode | Yes | Figure 2: the A algorithm for optimal subset selection |
| Open Source Code | No | The information is insufficient. The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We have tested the proposed algorithm on standard machine learning datasets. These were obtained from the UCI Repository, except for the techtc01 that can be obtained as explained in (Gabrilovich and Markovitch 2004). |
| Dataset Splits | No | The information is insufficient. The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The information is insufficient. The paper only mentions running on a 'standard desktop' without providing specific hardware details like exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The information is insufficient. The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The information is insufficient. The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |