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..
Fast and Flexible Temporal Point Processes with Triangular Maps
Authors: Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments", "Table 1: Average test set NLL on synthetic and real-world datasets (lower is better).", "Table 2: MMD between the hold-out test set and the generated samples (lower is better). |
| Researcher Affiliation | Academia | Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann Technical University of Munich, Germany EMAIL |
| Pseudocode | No | The paper describes algorithms and methods but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and datasets are available under www.daml.in.tum.de/triangular-tpp |
| Open Datasets | Yes | We use 6 synthetic datasets from Omi et al. [10]: Hawkes1&2 [7], self-correcting (SC) [16], inhomogeneous Poisson (IPP), renewal (RP) and modulated renewal (MRP) processes. ... We also consider 7 real-world datasets: PUBG (online gaming), Reddit-Comments, Reddit-Submissions (online discussions), Taxi (customer pickups), Twitter (tweets) and Yelp1&2 (check-in times). See Appendix D for more details. |
| Dataset Splits | Yes | We partitioned the sequences in each dataset into train/validation/test sequences (60%/20%/20%). We trained the models by minimizing the NLL of the train set using Adam [57]. We tuned the following hyperparameters: ... We used the validaiton set for hyperparameter tuning, early stopping and model development. |
| Hardware Specification | Yes | We used a machine with an Intel Xeon E5-2630 v4 @ 2.20 GHz CPU, 256GB RAM and an Nvidia GTX1080Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch [53]' for implementation but does not specify its version number, nor does it list versions for other software dependencies. |
| Experiment Setup | Yes | We tuned the following hyperparameters: L2 regularization {0, 10 5, 10 4, 10 3}, number of spline knots {10, 20, 50}, learning rate {10 3, 10 2}, hidden size {32, 64} for RNN, number of blocks {2, 4} and block size {8, 16} for Tri TPP. |