Structured Variational Inference in Continuous Cox Process Models
Authors: Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on synthetic and real-world data showing that its benefits are particularly pronounced on multivariate input settings where it overcomes the limitations of mean-field methods and sampling schemes. We provide the state of-the-art in terms of speed, accuracy and uncertainty quantification trade-offs. |
| Researcher Affiliation | Collaboration | Virginia Aglietti University of Warwick The Alan Turing Institute V.Aglietti@warwick.ac.uk Edwin V. Bonilla CSIRO s Data61 Edwin.Bonilla@data61.csiro.au Theodoros Damoulas University of Warwick The Alan Turing Institute T.Damoulas@warwick.ac.uk Sally Cripps Centre for Translational Data Science The University of Sydney Sally.Cripps@sydney.edu.au |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. The methods are described in prose and mathematical equations. |
| Open Source Code | Yes | Code and data for all the experiments is provided at https://github.com/Virgi Agl/STVB. |
| Open Datasets | Yes | Our first dataset is concerned with neuronal data, where event locations correspond to the position of a mouse moving in an arena when a recorded cell fired [5, 26]. As a second dataset, we consider the Porto taxi dataset3 which contains the trajectories of 7000 taxi travels in the years 2013/2014 in the city of Porto. As in [10], we consider the pick-up locations as observations of a PPP and restrict the analysis to events happening within the coordinates (41.147, 8.58) and (41.18, 8.65). We select N = 1000 events at random as training set and train the model with 400 inducing points placed on a regular grid. The test log likelihood is then computed on the remaining 3401 events. 3http://www.geolink.pt/ecmlpkdd2015-challenge/dataset.html. |
| Dataset Splits | No | For the neuronal data, the paper states: 'We randomly assign the events to either training (N = 583) or test (N = 29710)'. For the Porto Taxi dataset, it states: 'We select N = 1000 events at random as training set and train the model with 400 inducing points placed on a regular grid. The test log likelihood is then computed on the remaining 3401 events.' While training and testing splits are provided with specific counts, an explicit validation split is not mentioned. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or cloud computing specifications) used to run the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | For LGCP, we discretize the input space considering a grid cell width of one for λ1(x) and λ3(x) and of 0.5 for λ2(x). For MFVB we consider 1000 integration points. In terms of q({ym}M m=1|M), we set S = 5 but consistent results where found across different values of this parameter. and we run the model using a regular grid of 10 10 inducing inputs. and train the model with 400 inducing points placed on a regular grid. |