Revisiting the Approximation Bound for Stochastic Submodular Cover
Authors: Lisa Hellerstein, Devorah Kletenik
JAIR 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We revisit the proof of the k(ln R +1) bound of Deshpande et al., fill in the details of the proof of a key lemma, and prove two bounds for real-valued utility functions: k(ln R1 + 1) and (ln RE + 1). |
| Researcher Affiliation | Academia | Lisa Hellerstein EMAIL Department of Computer Science and Engineering NYU Tandon School of Engineering 2 Metrotech Center Brooklyn, NY 11201 Devorah Kletenik EMAIL Department of Computer and Information Science Brooklyn College, City University of New York 2900 Bedford Avenue Brooklyn, NY 11210 |
| Pseudocode | Yes | We give pseudocode in Figure 1, where we use xj to denote the random state of item j. ... Algorithm 1: Adaptive Greedy |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical, presenting proofs and approximation bounds for the Stochastic Submodular Cover problem. It does not describe experiments using specific datasets, thus no information on open datasets is provided. |
| Dataset Splits | No | The paper does not involve empirical experiments with datasets; therefore, there is no discussion of dataset splits. |
| Hardware Specification | No | The paper is a theoretical work focusing on mathematical proofs and algorithm analysis, and does not report on any experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | As a theoretical paper focused on mathematical proofs and algorithmic analysis, the authors do not describe any experimental setup involving specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and approximation bounds for an algorithm. It does not describe any empirical experiments or their setup; therefore, no specific experimental setup details such as hyperparameters are mentioned. |