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 [1].
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Authors: Muhammad Osama, Dave Zachariah, Peter Stoica
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The method is demonstrated using synthetic as well as real spatial data. |
| Researcher Affiliation | Academia | Muhammad Osama EMAIL Dave Zachariah EMAIL Peter Stoica EMAIL *Division of System and Control, Department of Information Technology, Uppsala University |
| Pseudocode | Yes | Algorithm 1 Conformal intensity interval; Algorithm 2 Majorization-minimization method |
| Open Source Code | Yes | The code for algorithms 1 and 2 are provided on github. |
| Open Datasets | Yes | First, we consider the hickory trees data set [1] which consists of coordinates of hickory trees in a spatial domain X Ă R2 [...] [1] P. J. Diggle @ lancaster university. https://www.lancaster.ac.uk/staff/diggle/pointpatternbook/datasets/. [...] Next we consider crime data in Portland police districts [16, 10] which consists of locations of calls-of-service received by Portland Police between January and March 2017 [...] [16] National Institute of Justice. Real-time crime forecasting challenge posting. https://nij.gov/funding/Pages/fy16-crime-forecasting-challenge-document.aspx#data. |
| Dataset Splits | No | The paper does not explicitly provide specific training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined split citations). It refers to 'out-of-sample' performance but no partitioning details. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions that code is provided on GitHub but does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper mentions specific regularization weights (γ=0.499, γ=0.4) in its numerical experiments, but it does not provide comprehensive experimental setup details such as learning rates, batch sizes, optimizer settings, or other hyperparameters required for full reproducibility. |