Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
Authors: Siqi Liu, Milos Hauskrecht
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the improved predictive performance of our model against state-of-the-art baselines on multiple synthetic and real-world datasets. 5 Experiments We compare our method with two state-of-the-art nonparametric Hawkes process variants. |
| Researcher Affiliation | Academia | Siqi Liu Department of Computer Science University of Pittsburgh Pittsburgh, PA 15213 siqiliu@cs.pitt.edu Milos Hauskrecht Department of Computer Science University of Pittsburgh Pittsburgh, PA 15213 milos@pitt.edu |
| Pseudocode | No | The paper describes algorithmic complexity and details but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper includes a footnote linking to 'https://github.com/HongtengXu/Hawkes-Process-Toolkit', which is a toolkit for Hawkes Processes, but does not provide an explicit statement or link for the open-source code of the method proposed in this paper. |
| Open Datasets | Yes | To show the flexibility of CGPRPP in modeling other complex event patterns than the bursty patterns as in many previously used datasets similar to the IPTV dataset, we derive multiple new event sequence datasets from MIMIC III [Johnson et al., 2016] consisting of lab tests ordered to patients in a hospital. |
| Dataset Splits | No | The paper mentions a validation set only in the context of tuning a baseline model (NSMMPP): 'The number of hidden units is selected from 64, 128, . . . , 1024 as in the original work through a validation set (80/20 split from the full training set)'. It does not explicitly provide validation splits for its own proposed method or for all experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, such as libraries or solvers, needed to replicate the experiments. |
| Experiment Setup | Yes | For the first dataset, we set Q = 1 and conditional points at 0, 5, . . . , 15 for CGPRPP. For HP-GS, the kernels are also placed at 0, 5, . . . , 15. For HP-LS, we set h = 1, k = 20. |