Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
Authors: Jinzhi Bu, David Simchi-Levi, Yunzong Xu
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,502 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,841 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 <EMAIL>, David Simchi-Levi <EMAIL>, Yunzong Xu <EMAIL>. |
| 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. |