Imputing Missing Events in Continuous-Time Event Streams
Authors: Hongyuan Mei, Guanghui Qin, Jason Eisner
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment in multiple synthetic and real domains, using different missingness mechanisms, and modeling the complete sequences in each domain with a neural Hawkes process (Mei & Eisner, 2017). On held-out incomplete sequences, our method is effective at inferring the groundtruth unobserved events, with particle smoothing consistently improving upon particle filtering. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Johns Hopkins University, USA 2Department of Physics, Peking University, China. Correspondence to: Hongyuan Mei <hmei@cs.jhu.edu>. |
| Pseudocode | Yes | Full details are spelled out in Algorithm 1 in Appendix C. ... Algorithm 2 in Appendix E uses dynamic programming to compute the loss (10) and its corresponding alignment a... Our heuristic (Algorithm 3 of Appendix F) seeks to iteratively improve ˆz... |
| Open Source Code | Yes | Py Torch code can be found at https://github.com/ HMEIat JHU/neural-hawkes-particle-smoothing. |
| Open Datasets | Yes | Elevator System Dataset (Crites & Barto, 1996). ... New York City Taxi Dataset (Whong, 2014). |
| Dataset Splits | Yes | For each of the datasets, we possess fully observed data that we use to train the model and the proposal distribution. ... and training is early-stopped when the divergence stops decreasing on the held-out development set. For each dev and test example, we censored out some events from the fully observed sequence |
| Hardware Specification | Yes | We also thank NVIDIA Corporation for kindly donating two Titan X Pascal GPUs and the state of Maryland for the Maryland Advanced Research Computing Center. |
| Software Dependencies | No | The paper states 'Py Torch code can be found at...' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | No | The paper states: 'See Appendix G for training details (e.g., hyperparameter selection).' This indicates that such details are present but are deferred to an appendix, not explicitly in the main text. |