Semi-Parametric Dynamic Contextual Pricing
Authors: Virag Shah, Ramesh Johari, Jose Blanchet
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically test a scalable implementation of our algorithm and observe good performance. |
| Researcher Affiliation | Academia | Virag Shah Management Science and Engineering Stanford University California, USA 94305 virag@stanford.edu |
| Pseudocode | No | formal definitions are provided in Appendix B to save space. The provided text does not include Appendix B, thus no pseudocode is present in this excerpt. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | First, we simulate our model with covariate dimension d = 2, where covariate vectors are i.i.d. d-dimensional standard normal random vectors, the parameter space is = [0, 1]d, the parameter vector is 0 = (1/√2), the noise support is Z = [0, 1], and the noise distribution is Z Uniform([0, 1]). |
| Dataset Splits | No | The paper describes generating synthetic data for simulations but does not specify training, validation, or test dataset splits for a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using a "semi-parametric regression technique from Plan and Vershynin (2013)" but does not provide specific software names with version numbers. |
| Experiment Setup | Yes | In this setting, we simulate policies DEEP-C, Decoupled DEEP-C, and Sparse DEEP-C for time horizon n = 10, 000 and for different values of parameter γ. Each policy is simulated 5,000 times for each set of parameters. Next, we also simulate our model for d = 100 with s = 4 non-zero entries in 0, with each non-zero entry equal to 1/ps, each policy is simulated 1,500 times for each set of parameters, with the rest of the setup being the same as earlier. |