Learning Registered Point Processes from Idiosyncratic Observations
Authors: Hongteng Xu, Lawrence Carin, Hongyuan Zha
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world data demonstrate that the method yields explainable point process models, achieving encouraging results compared to state-of-the-art methods. To demonstrate the feasibility and effectiveness of the proposed methods, we compare them to existing point process learning and registration methods on both synthetic and real-world datasets. |
| Researcher Affiliation | Collaboration | Hongteng Xu 1 2 Lawrence Carin 1 Hongyuan Zha 3 1Department of ECE, Duke University, Durham, NC, USA 2Infinia ML Inc., Durham, NC, USA 3College of Computing, Georgia Institute of Technology, Atlanta, GA, USA. |
| Pseudocode | No | The paper describes the proposed method in Section 3 “Learning Registered Point Processes” but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not contain any statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We test our methods and compare it with the WLR on two real-world datasets: the MIMIC III dataset (Johnson et al., 2016) and the Linkedin dataset (Xu et al., 2017a). |
| Dataset Splits | No | For each synthetic data set, we generate 200 event sequences in the time window [0, 100] using Ogata s thinning method (Ogata, 1981) and divide them equally into a training set and a testing set. The paper specifies training and testing sets but does not explicitly mention a separate validation set or its split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using an EM-based framework, an interior-point method, and kernel density estimation, but does not provide specific version numbers for any software, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | We investigate the robustness of our method to variations in its parameters, including the weight of regularizer γ and the number of landmarks L. In particular, we learn models from the synthetic data by our method with different configurations, and visualize the estimation errors with respect to these two parameters in Fig. 5. |