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..
On Subset Selection with General Cost Constraints
Authors: Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on sensor placement and influence maximization with both cardinality and routing constraints exhibit the superior performance of POMC. |
| Researcher Affiliation | Academia | 1UBRI, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China 2National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1 Generalized Greedy Algorithm. Algorithm 2 POMC Algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of its own methodology's code. |
| Open Datasets | Yes | We use two real-world data sets: one (http://db.csail.mit.edu/labdata/labdata.html) is collected from sensors installed at 55 locations of the Intel Berkeley Research lab; the other [Zheng et al., 2013] is air quality data collected from 36 monitoring stations in Beijing. For cardinality constraints, we use a real-world data set (http://www.isi.edu/lerman/downloads/digg2009.html) collected from the social news website Digg. |
| Dataset Splits | No | The paper uses datasets for experiments but does not explicitly specify training, validation, or test splits with percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | As POMC is a randomized algorithm, we repeat the run 10 times independently and report the average results. For cardinality and routing constraints, the budget B is set as {5, 6, . . . , 10} and {0.5, 0.6, . . . , 1}, respectively. For estimating the influence spread, i.e., the expected number of active nodes, we simulate the diffusion process 1,000 times independently and use the average as an estimation. For the number T of iterations of POMC, we used en BPmax/δˆc suggested by Theorem 2. |