Learning Hawkes Processes from a handful of events
Authors: Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real datasets show that our approach significantly outperforms state-of-the-art methods under short observation sequences. |
| Researcher Affiliation | Academia | Farnood Salehi EPFL farnood.salehi@epfl.ch William Trouleau EPFL william.trouleau@epfl.ch Matthias Grossglauser EPFL matthias.grossglauser@epfl.ch Patrick Thiran EPFL patrick.thiran@epfl.ch |
| Pseudocode | Yes | Algorithm 1 summarizes the proposed variational EM approach. |
| Open Source Code | Yes | Source code is available publicly. |
| Open Datasets | Yes | 1. Epidemics. This dataset contains records of infection of individuals, along with their correspond-ing district of residence, during the last Ebola epidemic in 2014-2015 [15]. 2. Stock market. This dataset contains the stock prices of 12 high-tech companies sampled every 2 minutes on the New York Stock Exchange for 20 days in April 2008 [13]. 3. Enron email. This dataset contains emails between employees of Enron from the Enron corpus. |
| Dataset Splits | No | To do so, we use the first 70% events as training set, and we compute the held-out averaged log-likelihood on the remaining 30%. This indicates a train/test split, but no explicit validation split or cross-validation is mentioned. |
| Hardware Specification | No | The paper does not explicitly specify any hardware details such as GPU/CPU models, processors, or memory used for experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for its dependencies. |
| Experiment Setup | Yes | For reproducibility, a detailed description of the experimental setup is provided in Appendix E. |