Online Pricing with Offline Data: Phase Transition and Inverse Square Law
Authors: Jinzhi Bu, David Simchi-Levi, Yunzong Xu
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct computational experiments to validate our theoretical results. See 7. ... We conduct a numerical study on a synthetic data set to test the performance of our algorithm. ... The numerical results also provide empirical evidence for phase transitions and the inverse-square law. |
| Researcher Affiliation | Academia | 1 Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139. Correspondence to: Jinzhi Bu <jzbu@mit.edu>, David Simchi-Levi <dslevi@mit.edu>, Yunzong Xu <yxu@mit.edu>. |
| Pseudocode | Yes | Algorithm 1 O3FU Algorithm and Algorithm 2 T-O3FU Algorithm |
| Open Source Code | No | Finally, we remark that the full version of this paper (containing additional theoretical results, computational experiments, and missing proofs) is available at https://arxiv.org/abs/1910.08693. This link is to the arXiv paper itself, not explicitly to source code. |
| Open Datasets | No | We conduct a numerical study on a synthetic data set to test the performance of our algorithm. No access information is provided for this synthetic dataset. |
| Dataset Splits | No | The paper mentions conducting a numerical study on a 'synthetic data set' but does not provide specific details on how this dataset was split for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments are mentioned. |
| Software Dependencies | No | No specific software dependencies (e.g., library or solver names with version numbers) are mentioned in the paper. |
| Experiment Setup | No | The paper describes algorithms and mentions a numerical study, but it does not provide concrete hyperparameter values or detailed training configurations for the experiments. |