Optimal Sampling Gaps for Adaptive Submodular Maximization
Authors: Shaojie Tang, Jing Yuan8450-8457
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to evaluate the impact of probability sampling based on two popular machine learning applications: adaptive viral marketing and pool-based active learning [Golovin and Krause 2011]. ... We present the results in Figure 1 and Figure 2. We observe that the performance trends of the algorithms are overall consistent between all datasets and application domains. |
| Researcher Affiliation | Academia | Shaojie Tang,1 Jing Yuan, 2 1Naveen Jindal School of Management, University of Texas at Dallas 2 Department of Computer Science, University of Texas at Dallas shaojie.tang@utdallas.edu, csyuanjing@gmail.com |
| Pseudocode | No | The paper describes algorithms (e.g., adaptive greedy algorithm) but does not provide them in structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing open-source code for its methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We capture the social network by a directed weighted graph and run experiments on four large-scale benchmark social networks: Wikivote, Net HEPT, Net PHY and Epinions (http://snap.stanford.edu/data/). |
| Dataset Splits | No | The paper mentions training models and using datasets but does not explicitly specify training, validation, or test splits with percentages, counts, or references to standard split methodologies. |
| Hardware Specification | No | The paper mentions running experiments and simulations but does not specify any hardware details such as GPU models, CPU models, or memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as Python versions or library versions. |
| Experiment Setup | Yes | We set k = 10 for Epinions, and k = 20 for other three datasets. ... We sample each node independently at a sampling rate r, that is, each node is being sampled independently with probability equal to r. We vary r from 0.1 to 1. ... For each sampling rate, we obtain 30 samples, and report the average performance of each algorithm over these samples... |