Hawkes Process Based on Controlled Differential Equations
Authors: Minju Jo, Seungji Kook, Noseong Park
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments with 4 real-world datasets, our method outperforms existing methods by non-trivial margins. |
| Researcher Affiliation | Academia | Minju Jo , Seungji Kook and Noseong Park Yonsei University, Seoul, South Korea {alflsowl12,202132139,noseong}@yonsei.ac.kr |
| Pseudocode | Yes | Algorithm 1 How to train HP-CDE |
| Open Source Code | No | The paper provides links to the source code of baseline models but does not explicitly state that the code for their proposed method (HP-CDE) is open-source or provide a link to it. |
| Open Datasets | Yes | Meme Tracker [Leskovec and Krevl, June 2014], Retweet [Zhao et al., 2015], and Stack Over Flow [Leskovec and Krevl, June 2014], are collected from Stackoverflow, web articles, and Twitter, respectively. We also use a medical dataset, called MIMIC [Johnson et al., 2016]. |
| Dataset Splits | No | The paper states, 'Each dataset is split into the training set and the testing set. The training set is used to tune the hyperparameters and the testing set is used to measure the model performance.' While hyperparameter tuning often implies a validation set, a distinct 'validation set' or specific split percentages for it are not explicitly provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') needed to replicate the experiment. |
| Experiment Setup | No | The paper states, 'More details including hyperparameter configurations are in Appendix C.', indicating that these details are not present in the main text of the paper. |