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..
Imputing Missing Events in Continuous-Time Event Streams
Authors: Hongyuan Mei, Guanghui Qin, Jason Eisner
ICML 2019 | Venue PDF | 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 ๏ฌltering. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Johns Hopkins University, USA 2Department of Physics, Peking University, China. Correspondence to: Hongyuan Mei <EMAIL>. |
| 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 หz... |
| 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. |