Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Learning for Multivariate Hawkes Processes
Authors: Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
NeurIPS 2017 | Venue PDF | 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. |