Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
Authors: Kaito Fujii, Shinsaku Sakaue
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments confirm that the greedy policy performs well with the applications being considered compared to standard heuristics. |
| Researcher Affiliation | Collaboration | 1University of Tokyo 2NTT Communication Science Laboratories. |
| Pseudocode | Yes | Algorithm 1 Adaptive greedy algorithm (Golovin & Krause, 2011) |
| Open Source Code | No | The paper provides a URL for a Yahoo! webscope dataset, but it does not include any explicit statements or links to the authors' own source code for the methodology described in the paper. |
| Open Datasets | Yes | Yahoo! webscope dataset: G1 Yahoo! Search Marketing Advertiser-Phrase Bipartite Graph, Version 1.0. URL https://webscope.sandbox.yahoo.com/. |
| Dataset Splits | No | The paper mentions using synthetic datasets and the Yahoo! dataset but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or explicit references to standard splits for reproducibility). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or cloud computing instance specifications. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their exact versions). |
| Experiment Setup | Yes | The probability that each edge (v, u) A is alive is set to the reciprocal of the degree of the sink vertex, that is, 1/|δ (v)|. ... In our experiments, parameter t is set to 3. ... We consider two settings: σ = 0.1 and 0.2. ... In all settings, we set n = 1000 and m = 100. |