Learnability of Competitive Threshold Models
Authors: Yifan Wang, Guangmo Tong
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results promisingly show that our method enjoys a decent performance without using excessive data points, outperforming off-the-shelf methods. |
| Researcher Affiliation | Academia | Yifan Wang and Guangmo Tong Department of Computer and Information Sciences, University of Delaware, USA {yifanw, amotong}@udel.edu |
| Pseudocode | No | The paper describes the model and algorithms using mathematical formulas and descriptive text, but it does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The proofs, full experimental results, and source code are provided in the supplementary material1. 1https://github.com/cdslabamotong/LTInf Learning |
| Open Datasets | Yes | We adopt two classic graph structures (Kronecker [Leskovec et al., 2010] and power-law) and one real graph Higgs, which was built after monitoring the spreading process... [De Domenico et al., 2013]. |
| Dataset Splits | No | The paper states 'The size of the training set is selected from {50, 100, 500} and the testing size is 500' but does not explicitly mention a validation set or how data is split for validation. |
| Hardware Specification | No | The paper mentions software used (Cplex, scikit-learn) but does not provide specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | Yes | Given the samples, the parameters of our model are computed by the linear programming solver Cplex [Cplex, 2009]. [...] we implement three methods, logistic regression (LR), support vector machine (SVM), and multilayer perceptron (MLP), using scikit-learn [Pedregosa et al., 2011]. |
| Experiment Setup | Yes | The thresholds θs i are generated uniformly at random from [0, 1]. [...] The parameters are set to be numbers with three decimal places. [...] The size of the training set is selected from {50, 100, 500} and the testing size is 500. [...] The entire process is repeated for five times, and we report the average performance together with the standard deviation. |