Proper Loss Functions for Nonlinear Hawkes Processes
Authors: Aditya Menon, Young Lee
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical analyses by illustrating the viability of using losses other than the standard maximum likelihood to fit various (non-)linear Hawkes processes. |
| Researcher Affiliation | Academia | Aditya Krishna Menon Data61 and the Australian National University aditya.menon@data61.csiro.au National University of Singapore dcsleey@nus.edu.sg |
| Pseudocode | Yes | Figure 1: Framework to fit nonlinear Hawkes processes. INPUT: Invertible nonlinearity F( ); kernels {ki}L i=1 PROCEDURE: (1) Construct canonical proper loss per Equation 24, or alternate loss with link per Equation 23 (2) Find the linear scorer that minimises 22 (3) Estimate intensity using Equation 18 |
| Open Source Code | No | The paper does not provide concrete access to its own source code. It cites a third-party GitHub repository (Heusser 2013) which seems to contain the bitcoin dataset, not for the authors' own methodology. |
| Open Datasets | Yes | We compare the various losses on two real-world datasets: Last FM (Celma 2010), comprising the times that users listen to songs by artists... and bitcoin, comprising times of trades on the Mt Gox Bitcoin exchange for a single day (Heusser 2013). |
| Dataset Splits | Yes | For each dataset, we split the recorded event times into a train and test set. ... For Last FM, we select 500 random (user, artist) pairs for which there are at least 100 listening events over the span of at least two months. For each pair, we define the testing period to be the last month of the recorded history. For bitcoin, we use all trades occurring in the window 1PM 3PM for training, and make predictions in the window 3PM 4PM. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. It only mentions the use of 'L-BFGS' for optimization. |
| Experiment Setup | Yes | We optimised the MLE and (modified) logistic loss with L-BFGS, enforcing a stationarity constraint that α < δ and a tolerance criterion of 10 8. For the logistic loss, we apply a weighting w = 108 on the background class, following Equation 28; for other losses, we set w = 1. |