Diffusion-aware Censored Gaussian Processes for Demand Modelling
Authors: Filipe Rodrigues
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper proposes Diffusion-aware Censored Demand Models, which combine a Tobit likelihood with a graph-based diffusion process in order to model the latent process of transfer of unsatisfied demand between similar products or services. We instantiate this new class of models under the framework of GPs and, based on both simulated and real-world data for modeling sales, bike-sharing demand, and EV charging demand, demonstrate its ability to better recover the true demand and produce more accurate out-of-sample predictions. |
| Researcher Affiliation | Academia | Filipe Rodrigues Technical University of Denmark EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical equations and textual explanations, but it does not include any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | Yes | Source code for all the experiments is provided at: https://bit.ly/3E2kdSJ |
| Open Datasets | Yes | We now turn to experiments on real-world supermarket vegetable sales data obtained from [Sup]. ... [Sup, 2024] Sup. Supermarket sales data: Sales data of vegetables in supermarket. https://www.kaggle.com/datasets/ yapwh1208/supermarket-sales-data, 2024. Accessed: 2024-04-23. |
| Dataset Splits | Yes | We randomly sample 90% observations for training and leave 10% for testing. ... We hold out 30% of data for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solver versions) used in the experiments. |
| Experiment Setup | Yes | The hyper-parameters of the likelihood in Eq. 9 are then: the observation variance σ2, the diffusion lengthscale ℓdiff, the sink node parameter πdiff, and the number of diffusion steps Ndiff. ... for Datasets A and C, the best results were obtained with a fixed ℓdiff = 1, while for Dataset B learning ℓdiff resulted in slightly better results. ... We simulate the censoring process using a 2-state Markov model... with fixed diffusion parameters and with a sink node probability of 20%. |