Improved Algorithms for Contextual Dynamic Pricing
Authors: Matilde Tullii, Solenne Gaucher, Nadav Merlis, Vianney Perchet
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we illustrate some numerical simulations that aim to show the empirical performance of our VAPE algorithm for Linear Valuations. |
| Researcher Affiliation | Collaboration | Matilde Tullii Fair Play Team, CREST, ENSAE Solenne Gaucher Fair Play Team, CREST, ENSAE Nadav Merlis Fair Play Team, CREST, ENSAE Vianney Perchet Fair Play Team, CREST, ENSAE Criteo AI Lab |
| Pseudocode | Yes | Algorithm 1 VALUATION APPROXIMATION PRICE ELIMINATION (VAPE): General scheme |
| Open Source Code | Yes | The code implemented for these simulations is publicly available in the repository: https://github.com/Matilde Tulii1/ Improved-Algorithms-for-Contextual-Dynamic-Pricing |
| Open Datasets | No | In order to test our algorithm, we built a dataset of 5 contexts belonging to R3 generated by a canonical gaussian distribution and subsequently normalized. |
| Dataset Splits | No | The paper discusses simulation runs over specified time horizons (e.g., T [1000, 10000, 50000, 200000, 500000, 800000]) and uses a 'stochastic' and 'adversarial' case for comparison with previous work, but it does not specify explicit train/validation/test dataset splits or cross-validation methodology. |
| Hardware Specification | Yes | All the simulation can be (and were) run on a laptop without gpus. |
| Software Dependencies | No | The paper provides a GitHub repository link for the code, but it does not explicitly list specific software dependencies with their version numbers within the paper itself. |
| Experiment Setup | Yes | The algorithm has been tested on time horizons T [1000, 10000, 50000, 200000, 500000, 800000], and the hyperparameters α, µ, ϵ are set as in the statement of Theorem 1. |