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.
Optimal Sampling Gaps for Adaptive Submodular Maximization
Authors: Shaojie Tang, Jing Yuan8450-8457
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,443 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,865 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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... |