Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Maximization of Approximately Submodular Functions
Authors: Thibaut Horel, Yaron Singer
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 14,470 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,543 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide both lower and upper bounds: for ε > n 1/2 we show an exponential query-complexity lower bound. In contrast, when ε < 1/k or under a stronger bounded curvature assumption, we give constant approximation algorithms. |
| Researcher Affiliation | Academia | Thibaut Horel Harvard University EMAIL Yaron Singer Harvard University EMAIL |
| Pseudocode | No | The paper describes the 'greedy algorithm' and refers to its 'detailed description... in the appendix', but no explicit pseudocode or algorithm block is present in the provided text. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets, thus no information on dataset availability or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with data, so no information about training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe implementations or require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |