Online Learning for Multivariate Hawkes Processes
Authors: Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results show that our algorithm offers a competing performance to that of the nonparametric batch learning algorithm, with a run time comparable to parametric online learning algorithms. We evaluate the performance of NPOLE-MHP on both synthetic and real data, from multiple aspects: (i) visual assessment of the goodness-of-fit comparing to the ground truth; (ii) the average L1 error defined as the average of Pp i=1 Pp j=1 fi,j bfi,j L1[0,z] over multiple trials; (iii) scalability over both dimension p and time horizon T. For benchmarks, we compare NPOLE-MHP s performance to that of online parametric algorithms (DMD, OGD of [15]) and nonparametric batch learning algorithms (MLE-SGLP, MLE of [27]). |
| Researcher Affiliation | Academia | University of Illinois at Urbana-Champaign Urbana, IL 61801 {yyang172,etesami2,niaohe,kiyavash} @illinois.edu |
| Pseudocode | Yes | Algorithm 1 Non Parametric On Line Estimation for MHP (NPOLE-MHP) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or mention of code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | We test the performance of NPOLE-MHP on the memetracker data [21] |
| Dataset Splits | No | The paper mentions 'training and test data' but does not provide specific details on how the dataset was split (e.g., explicit percentages, sample counts, or details on cross-validation setup). |
| Hardware Specification | Yes | The simulation of the DMD and OGD algorithms took 2 minutes combined on a Macintosh with two 6-core Intel Xeon processor at 2.4 GHz, while NPOLE-MHP took 3 minutes. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., programming language versions, library versions, or specific solver versions) needed to replicate the experiment. |
| Experiment Setup | Yes | In particular, we set the discretization level δ = 0.05, the window size z = 3, the step size ηk = (kδ/20+100) 1, and the regularization coefficient ζi,j ζ = 10 8. using a window size of 3 hours, an update interval δ = 0.2 seconds, and a step size ηk = 1/(kζ + 800) with ζ = 10 10 for NPOLE-MHP. |