Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences
Authors: Dixin Luo, Hongteng Xu, Yi Zhen, Xia Ning, Hongyuan Zha, Xiaokang Yang, Wenjun Zhang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that MMHP performs well on both synthetic and real data. |
| Researcher Affiliation | Academia | Dixin Luo1 , Hongteng Xu2 , Yi Zhen2, Xia Ning3, Hongyuan Zha2,4, Xiaokang Yang1, Wenjun Zhang1 1SEIEE, Shanghai Jiao Tong University, Shanghai, China 2College of Computing, Georgia Institute of Technology, Atlanta, GA, USA 3Department of Computer and Information Science, IUPUI, Indianapolis, IN, USA 4Software Engineering Institute, East China Normal University, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 MMHP Learning Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We simulate 100 training sequences and 100 testing sequences respectively. Each training sequence contains 2500 events... The dataset is collected from China Telecom, in Shanghai, China [Luo et al., 2014] |
| Dataset Splits | No | The paper does not explicitly describe a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | With the identified optimal configuration as λ1 = 0.2, λ2 = 0.004, M = 500, α = 100, we fix 3 parameters as their optimal values each time and alter the fourth parameter to train a different MMHP model. ...For all the methods, we set the length of the triggering kernel as 11520 minutes (8 days) and the sampling interval t as 20 minutes(M = 576). |